FhVLAD: Fine-grained quantization and encoding high-order descriptor statistics for scalable image retrieval

We are interested in the encoding of local descriptors of an image ( e.g. SIFT) to design a compact representation vector and thereby address scalable image retrieval. We revisit the implicit design choices in the popular vector of locally aggregated descriptors (VLAD), which aggregates the residual...

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Vydané v:	Multimedia tools and applications Ročník 80; číslo 28-29; s. 35495 - 35520
Hlavní autori:	Bhowmick, Alexy, Saharia, Sarat, Hazarika, Shyamanta M.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.11.2021 Springer Nature B.V
Predmet:	1166: Advances of machine learning in data analytics and visual information processing Computer Communication Networks Computer Science Data Structures and Information Theory Image management Image retrieval Measurement Multimedia Information Systems Special Purpose and Application-Based Systems Statistics VLAD Fine-grained Residual High-order Image retrieval
ISSN:	1380-7501, 1573-7721
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Abstract	We are interested in the encoding of local descriptors of an image ( e.g. SIFT) to design a compact representation vector and thereby address scalable image retrieval. We revisit the implicit design choices in the popular vector of locally aggregated descriptors (VLAD), which aggregates the residuals of descriptors to the codewords. VLAD’s use of a coarse codebook and first-order descriptor statistics in residual computation results in less discriminative residuals. To address this problem, we propose a division of codebook feature space using a novel fine-grained quantization strategy. After quantization, we embed the resulting residuals with high-order statistics of descriptor distribution. Experiments on three challenging image retrieval datasets (INRIA Holidays, UKBench, Oxford 5k) confirm the improved discriminative power of our novel encoding method called FhVLAD. We observe superior accuracy to baseline and competitive performance to state-of-the-art techniques with a limited increase in dimension.
AbstractList	We are interested in the encoding of local descriptors of an image (e.g. SIFT) to design a compact representation vector and thereby address scalable image retrieval. We revisit the implicit design choices in the popular vector of locally aggregated descriptors (VLAD), which aggregates the residuals of descriptors to the codewords. VLAD’s use of a coarse codebook and first-order descriptor statistics in residual computation results in less discriminative residuals. To address this problem, we propose a division of codebook feature space using a novel fine-grained quantization strategy. After quantization, we embed the resulting residuals with high-order statistics of descriptor distribution. Experiments on three challenging image retrieval datasets (INRIA Holidays, UKBench, Oxford 5k) confirm the improved discriminative power of our novel encoding method called FhVLAD. We observe superior accuracy to baseline and competitive performance to state-of-the-art techniques with a limited increase in dimension. We are interested in the encoding of local descriptors of an image ( e.g. SIFT) to design a compact representation vector and thereby address scalable image retrieval. We revisit the implicit design choices in the popular vector of locally aggregated descriptors (VLAD), which aggregates the residuals of descriptors to the codewords. VLAD’s use of a coarse codebook and first-order descriptor statistics in residual computation results in less discriminative residuals. To address this problem, we propose a division of codebook feature space using a novel fine-grained quantization strategy. After quantization, we embed the resulting residuals with high-order statistics of descriptor distribution. Experiments on three challenging image retrieval datasets (INRIA Holidays, UKBench, Oxford 5k) confirm the improved discriminative power of our novel encoding method called FhVLAD. We observe superior accuracy to baseline and competitive performance to state-of-the-art techniques with a limited increase in dimension.
Author	Hazarika, Shyamanta M. Saharia, Sarat Bhowmick, Alexy
Author_xml	– sequence: 1 givenname: Alexy orcidid: 0000-0003-2334-5856 surname: Bhowmick fullname: Bhowmick, Alexy email: alexy.bhowmick@gmail.com organization: Department of Computer Science and Engineering, Assam Don Bosco University, Department of Computer Science and Engineering, Tezpur University – sequence: 2 givenname: Sarat surname: Saharia fullname: Saharia, Sarat organization: Department of Computer Science and Engineering, Tezpur University – sequence: 3 givenname: Shyamanta M. surname: Hazarika fullname: Hazarika, Shyamanta M. organization: Biomimetic Robotics and Artificial Intelligence Lab, Department of Mechanical Engineering, Indian Institute of Technology
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Cites_doi	10.1023/B:VISI.0000029664.99615.94 10.1109/ICCV.2003.1238663 10.1145/2502081.2502171 10.1007/978-3-319-10590-1_38 10.1109/CVPR.2010.5540039 10.1109/MCSE.2018.108164530 10.1007/s11042-015-2524-6 10.3169/mta.4.251 10.1109/ICIP.2016.7532906 10.1109/CVPR.2016.175 10.1016/j.neucom.2017.11.022 10.1007/s11042-019-07771-2 10.1111/coin.12202 10.1109/CVPRW.2015.7301272 10.1080/00031305.1988.10475539 10.1186/s13640-018-0247-0 10.3390/e18080311 10.1007/s11263-009-0285-2 10.1109/TPAMI.2011.235 10.1109/ICCV.2015.19 10.1109/CVPR.2007.383266 10.1109/CVPR.2013.207 10.1007/s11042-015-2819-7 10.1109/CVPR.2008.4587635 10.1023/B:VISI.0000027790.02288.f2 10.1109/TIP.2017.2688133 10.1007/978-3-642-33709-3_55 10.1109/ICIP.2015.7351684 10.1109/TCSVT.2015.2409693 10.1109/CVPR.2014.417 10.1016/j.neucom.2015.08.076 10.1007/978-3-319-10578-9_43 10.1007/978-3-642-15561-1_11 10.1109/VCIP.2014.7051576 10.1007/11744023_32 10.1109/ICCV.2017.374 10.1109/CVPR.2009.5206757 10.1109/CVPR.2007.383172 10.1109/CVPRW.2014.131 10.1109/CVPR.2006.264 10.1109/TPAMI.2013.237 10.1109/ICIP.2014.7025610 10.1109/TPAMI.2017.2711011 10.1109/CVPR.2010.5540009 10.1109/CVPR.2012.6248018 10.1007/978-3-642-15555-0_11 10.1109/CVPR.2006.68 10.1007/978-3-030-34869-4_61 10.1109/CVPR.2010.5540018 10.1007/978-3-319-10584-0_26 10.1109/TPAMI.2016.2613873 10.1016/j.neucom.2016.12.002 10.5244/C.27.99 10.1007/978-3-540-88682-2_24
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References	Bhowmick A, Saharia S, Hazarika SM (2019) Encoding high-Order statistics in VLAD for scalable image retrieval. In: Deka B, Maji P, Mitra S, Bhattacharyya DK, Bora PK, Pal SK (eds) Pattern recognition and machine intelligence, lecture notes in computer science. Springer International Publishing, Cham, pp 559–566 Eggert C, Romberg S, Lienhart R (2014) Improving VLAD: Hierarchical coding and a refined local coordinate system. In: 2014 IEEE international conference on image processing (ICIP), pp 3018–3022 MironicăIDuţăICIonescuBSebeNA modified vector of locally aggregated descriptors approach for fast video classificationMultimedia Tools and Applications201675159045907210.1007/s11042-015-2819-7 WeiXSLuoJHWuJZhouZHSelective convolutional descriptor aggregation for fine-Grained image retrievalIEEE Trans Image Process201726628682881364867610.1109/TIP.2017.2688133 Ng JYH, Yang F, Davis LS (2015) Exploiting local features from deep networks for image retrieval. In: 2015 IEEE Conference on computer vision and pattern recognition workshops (CVPRW). IEEE, Boston, pp 53–61 Zhao WL, Gravier G, Jégou H (2013) Oriented pooling for dense and non-dense rotation-invariant features. In: Burghardt T, Damen D, Mayol-Cuevas WW, Mirmehdi M (eds) British Machine Vision Conference, BMVC 2013, Bristol, UK, September 9-13, 2013, BMVA Press Perronnin F, Dance C (2007) Fisher kernels on visual vocabularies for image categorization. In: 2007 IEEE conference on computer vision and pattern recognition, pp 1–8 Liu L, Wang L, Liu X (2011) In Defense of Soft-assignment Coding. In: Proceedings of the 2011 international conference on computer vision, ICCV ’11. IEEE Computer Society, Washington, pp 2486–2493 GaoWZhuYZhangWZhangKGaoHA hierarchical recurrent approach to predict scene graphs from a visual-attention-oriented perspectiveComput Intell2019353496516399649510.1111/coin.12202 MikolajczykKSchmidCScale & affine invariant interest point detectorsInt J Comput Vision2004601638610.1023/B:VISI.0000027790.02288.f2 Wang Y, Duan LY, Lin J, Wang Z, Huang T (2015) Hierarchical multi-VLAD for image retrieval. In: 2015 IEEE international conference on image processing (ICIP), pp 4629–4633 Philbin J, Chum O, Isard M, Sivic J, Zisserman A (2008) Lost in quantization: Improving particular object retrieval in large scale image databases. In: 2008 IEEE conference on computer vision and pattern recognition, pp 1–8 ZhouQWangCLiuPLiQWangYChenSDistribution entropy boosted VLAD for image retrievalEntropy201618831110.3390/e18080311 BalandaKPMacGillivrayHLKurtosis: a critical reviewThe American Statistician1988422111119 Gong Y, Wang L, Guo R, Lazebnik S (2014) Multi-scale orderless pooling of deep convolutional activation features. In: Fleet DJ, Pajdla T, Schiele B, Tuytelaars T (eds) Computer vision - ECCV 2014 - 13th european conference, zurich, switzerland, september 6-12, 2014, proceedings, Part VII, Springer, Lecture Notes in Computer Science, vol 8695, pp 392–407 Babenko A, Lempitsky V (2015) Aggregating local deep features for image retrieval. In: Proceedings of the 2015 IEEE international conference on computer vision (ICCV), IEEE Computer Society, USA, ICCV ’15, pp 1269–1277 Razavian AS, Azizpour H, Sullivan J, Carlsson S (2014) CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the 2014 IEEE conference on computer vision and pattern recognition workshops, IEEE Computer Society, USA, CVPRW ’14, pp 512–519 LiuPMiaoZGuoHWangYAiNAdding spatial distribution clue to aggregated vector in image retrievalEURASIP J Image Video Process201820181910.1186/s13640-018-0247-0 Noh H, Araujo A, Sim J, Weyand T, Han B (2017) Large-Scale image retrieval with attentive deep local features. In: IEEE International conference on computer vision, ICCV 2017, venice, italy, October 22-29, 2017, pp 3476–3485 YuWYangKYaoHSunXXuPExploiting the complementary strengths of multi-layer CNN features for image retrievalNeurocomputing201723723524110.1016/j.neucom.2016.12.002 Perronnin F, Liu Y, Sánchez J, Poirier H (2010) Large-scale image retrieval with compressed Fisher vectors. In: 2010 IEEE computer society conference on computer vision and pattern recognition, pp 3384–3391 Sivic J, Zisserman A (2003) Video Google: a text retrieval approach to object matching in videos. In: Proceedings Ninth IEEE international conference on computer vision, vol 2, pp 1470–1477 Bay H, Tuytelaars T, Gool LV (2006) SURF: speeded up robust features. In: Computer vision – ECCV 2006, Lecture Notes in Computer Science. springer, Berlin, pp 404–417 Razavian AS, Sullivan J, Carlsson S, Maki A (2016) Visual instance retrieval with deep convolutional networks. arXiv:14126574 [cs] Delhumeau J, Gosselin PH, Jégou H, Pérez P (2013) Revisiting the VLAD image representation, ACM, New York LiQPengQYanCMultiple VLAD encoding of CNNs for image classificationComput Sci Eng2018202526310.1109/MCSE.2018.108164530 Peng X, Wang L, Qiao Y, Peng Q (2014) Boosting VLAD with supervised dictionary learning and high-Order statistics. In: Computer vision – ECCV 2014, Lecture Notes in Computer Science. Springer, Cham, pp 660–674 BishopCPattern recognition and machine learning information science and statistics2006New YorkSpringer1107.68072 Jegou H, Douze M, Schmid C, Pérez P (2010) Aggregating local descriptors into a compact image representation. In: 2010 IEEE computer society conference on computer vision and pattern recognition, pp 3304–3311 LongXLuHPengYWangXFengSImage classification based on improved VLADMultimedia Tools and Applications201675105533555510.1007/s11042-015-2524-6 JegouHDouzeMSchmidCImproving bag-of-features for large scale image searchInt J Comput Vis201087331633610.1007/s11263-009-0285-2 Arandjelović R, Zisserman A (2012) Three things everyone should know to improve object retrieval. In: 2012 IEEE conference on computer vision and pattern recognition, pp 2911–2918 LiuZHouqiangLWengangZTingRQiTMaking residual vector distribution uniform for distinctive image representationIEEE Trans Circ Syst Video Technol201626237538410.1109/TCSVT.2015.2409693 Philbin J, Chum O, Isard M, Sivic J, Zisserman A (2007) Object retrieval with large vocabularies and fast spatial matching. In: 2007 IEEE conference on computer vision and pattern recognition, pp 1–8 Arandjelovic R, Zisserman A (2013) All About VLAD. In: 2013 IEEE conference on computer vision and pattern recognition, pp 1578–1585 Mairal J, Koniusz P, Harchaoui Z, Schmid C (2014) Convolutional kernel networks. In: Proceedings of the 27th international conference on neural information processing systems, NIPS’14, vol 2. MIT Press, Cambridge, pp 2627–2635 LoweDGDistinctive image features from scale-Invariant keypointsInt J Comput Vis20046029111010.1023/B:VISI.0000029664.99615.94 Sattler T, Havlena M, Schindler K, Pollefeys M (2016) Large-scale location recognition and the geometric burstiness problem. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 1582–1590 . https://doi.org/10.1109/CVPR.2016.175 Wang J, Yang J, Yu K, Lv F, Huang T, Gong Y (2010) Locality-constrained Linear Coding for image classification. In: 2010 IEEE computer society conference on computer vision and pattern recognition, pp 3360–3367 Zhou X, Yu K, Zhang T, Huang TS (2010) Image classification using super-Vector coding of local image descriptors. In: Computer vision – ECCV 2010, Lecture Notes in Computer Science. Springer, Berlin, pp 141–154 Paulin M, Douze M, Harchaoui Z, Mairal J, Perronin F, Schmid C (2015) Local convolutional features with unsupervised training for image retrieval. In: 2015 IEEE international conference on computer vision (ICCV), pp 91–99 Perronnin F, Sánchez J, Mensink T (2010) Improving the fisher kernel for large-scale image classification. In: Proceedings of the 11th European conference on computer vision: Part IV, ECCV’10. Springer, Berlin, pp 143–156 Tolias G, Sicre R, Jégou H (2016) Particular object retrieval with integral max-pooling of CNN activations. In: 4th international conference on learning representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings Zhou R, Yuan Q, Gu X, Zhang D (2014) Spatial pyramid VLAD. In: 2014 IEEE visual communications and image processing conference, pp 342–345 Jegou H, Douze M, Schmid C (2008) Hamming embedding and weak geometric consistency for large scale image search. In: Proceedings of the 10th European conference on computer vision: Part I, Springer, Berlin, Heidelberg, ECCV ’08, pp 304–317 ShenXLinZBrandtJWuYSpatially-Constrained similarity measurefor large-Scale object retrievalIEEE Trans Pattern Anal Mach Intell20143661229124110.1109/TPAMI.2013.237 Jegou H, Chum O (2012) Negative evidences and co-occurences in image retrieval: the benefit of PCA and whitening. In: Computer vision – ECCV 2012, Lecture Notes in Computer Science. Springer, Berlin, pp 774–787 JegouHPerronninFDouzeMSánchezJPérezPSchmidCAggregating local image descriptors into compact codesIEEE Trans Pattern Anal Mach Intell20123491704171610.1109/TPAMI.2011.235 LiuZWangSTianQFine-residual VLAD for Image RetrievalNeurocomput2016173P31183119110.1016/j.neucom.2015.08.076 ArandjelovićRGronatPToriiAPajdlaTSivicJNetVLAD: CNN architecture for weakly supervised place recognitionIEEE Trans Pattern Anal Mach Intell20184061437145110.1109/TPAMI.2017.2711011conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence Jegou H, Zisserman A (2014) Triangulation embedding and democratic aggregation for image search. In: Proc IEEE Conf computer vision and patter recognition, pp 3310–3317 Babenko A, Slesarev A, Chigorin A, Lempitsky VS (2014) Neural codes for image retrieval. In: Computer vision - ECCV 2014 - 13th european conference, zurich, Switzerland, September 6-12, 2014, Proceedings Part I, pp 584–599 Nister D, Stewenius H (2006) Scalable recognition with a vocabulary tree. 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References_xml	– reference: Zheng J, Chen JC, Bodla N, Patel VM, Chellappa R (2016) VLAD encoded Deep Convolutional features for unconstrained face verification. In: 2016 23rd international conference on pattern recognition (ICPR), pp 4101–4106 – reference: Philbin J, Chum O, Isard M, Sivic J, Zisserman A (2007) Object retrieval with large vocabularies and fast spatial matching. In: 2007 IEEE conference on computer vision and pattern recognition, pp 1–8 – reference: Gong Y, Wang L, Guo R, Lazebnik S (2014) Multi-scale orderless pooling of deep convolutional activation features. In: Fleet DJ, Pajdla T, Schiele B, Tuytelaars T (eds) Computer vision - ECCV 2014 - 13th european conference, zurich, switzerland, september 6-12, 2014, proceedings, Part VII, Springer, Lecture Notes in Computer Science, vol 8695, pp 392–407 – reference: JegouHDouzeMSchmidCImproving bag-of-features for large scale image searchInt J Comput Vis201087331633610.1007/s11263-009-0285-2 – reference: BalandaKPMacGillivrayHLKurtosis: a critical reviewThe American Statistician1988422111119 – reference: Zhou R, Yuan Q, Gu X, Zhang D (2014) Spatial pyramid VLAD. In: 2014 IEEE visual communications and image processing conference, pp 342–345 – reference: Eggert C, Romberg S, Lienhart R (2014) Improving VLAD: Hierarchical coding and a refined local coordinate system. In: 2014 IEEE international conference on image processing (ICIP), pp 3018–3022 – reference: Noh H, Araujo A, Sim J, Weyand T, Han B (2017) Large-Scale image retrieval with attentive deep local features. In: IEEE International conference on computer vision, ICCV 2017, venice, italy, October 22-29, 2017, pp 3476–3485 – reference: Perronnin F, Liu Y, Sánchez J, Poirier H (2010) Large-scale image retrieval with compressed Fisher vectors. In: 2010 IEEE computer society conference on computer vision and pattern recognition, pp 3384–3391 – reference: MironicăIDuţăICIonescuBSebeNA modified vector of locally aggregated descriptors approach for fast video classificationMultimedia Tools and Applications201675159045907210.1007/s11042-015-2819-7 – reference: LiuZHouqiangLWengangZTingRQiTMaking residual vector distribution uniform for distinctive image representationIEEE Trans Circ Syst Video Technol201626237538410.1109/TCSVT.2015.2409693 – reference: Perronnin F, Dance C (2007) Fisher kernels on visual vocabularies for image categorization. In: 2007 IEEE conference on computer vision and pattern recognition, pp 1–8 – reference: Bhowmick A, Saharia S, Hazarika SM (2019) Encoding high-Order statistics in VLAD for scalable image retrieval. In: Deka B, Maji P, Mitra S, Bhattacharyya DK, Bora PK, Pal SK (eds) Pattern recognition and machine intelligence, lecture notes in computer science. Springer International Publishing, Cham, pp 559–566 – reference: Babenko A, Slesarev A, Chigorin A, Lempitsky VS (2014) Neural codes for image retrieval. In: Computer vision - ECCV 2014 - 13th european conference, zurich, Switzerland, September 6-12, 2014, Proceedings Part I, pp 584–599 – reference: Zhou X, Yu K, Zhang T, Huang TS (2010) Image classification using super-Vector coding of local image descriptors. In: Computer vision – ECCV 2010, Lecture Notes in Computer Science. Springer, Berlin, pp 141–154 – reference: Jegou H, Chum O (2012) Negative evidences and co-occurences in image retrieval: the benefit of PCA and whitening. In: Computer vision – ECCV 2012, Lecture Notes in Computer Science. Springer, Berlin, pp 774–787 – reference: WuZYuJA multi-level descriptor using ultra-deep feature for image retrievalMultimedia Tools and Applications20197818256552567210.1007/s11042-019-07771-2 – reference: Yang J, Yu K, Gong Y, Huang T (2009) Linear spatial pyramid matching using sparse coding for image classification. In: 2009 IEEE conference on computer vision and pattern recognition, pp 1794–1801 – reference: LiuZWangSTianQFine-residual VLAD for Image RetrievalNeurocomput2016173P31183119110.1016/j.neucom.2015.08.076 – reference: Philbin J, Chum O, Isard M, Sivic J, Zisserman A (2008) Lost in quantization: Improving particular object retrieval in large scale image databases. In: 2008 IEEE conference on computer vision and pattern recognition, pp 1–8 – reference: BishopCPattern recognition and machine learning information science and statistics2006New YorkSpringer1107.68072 – reference: Mairal J, Koniusz P, Harchaoui Z, Schmid C (2014) Convolutional kernel networks. In: Proceedings of the 27th international conference on neural information processing systems, NIPS’14, vol 2. MIT Press, Cambridge, pp 2627–2635 – reference: TzelepiMTefasADeep convolutional learning for Content Based Image RetrievalNeurocomputing20182752467247810.1016/j.neucom.2017.11.022 – reference: LongXLuHPengYWangXFengSImage classification based on improved VLADMultimedia Tools and Applications201675105533555510.1007/s11042-015-2524-6 – reference: Paulin M, Douze M, Harchaoui Z, Mairal J, Perronin F, Schmid C (2015) Local convolutional features with unsupervised training for image retrieval. In: 2015 IEEE international conference on computer vision (ICCV), pp 91–99 – reference: Wang Y, Duan LY, Lin J, Wang Z, Huang T (2015) Hierarchical multi-VLAD for image retrieval. In: 2015 IEEE international conference on image processing (ICIP), pp 4629–4633 – reference: Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the 2006 IEEE computer society conference on computer vision and pattern recognition - Volume 2, IEEE Computer Society, USA, CVPR ’06, pp 2169–2178 – reference: Perronnin F, Sánchez J, Mensink T (2010) Improving the fisher kernel for large-scale image classification. In: Proceedings of the 11th European conference on computer vision: Part IV, ECCV’10. Springer, Berlin, pp 143–156 – reference: YuWYangKYaoHSunXXuPExploiting the complementary strengths of multi-layer CNN features for image retrievalNeurocomputing201723723524110.1016/j.neucom.2016.12.002 – reference: Peng X, Wang L, Qiao Y, Peng Q (2014) Boosting VLAD with supervised dictionary learning and high-Order statistics. In: Computer vision – ECCV 2014, Lecture Notes in Computer Science. Springer, Cham, pp 660–674 – reference: Liu L, Wang L, Liu X (2011) In Defense of Soft-assignment Coding. In: Proceedings of the 2011 international conference on computer vision, ICCV ’11. IEEE Computer Society, Washington, pp 2486–2493 – reference: Tolias G, Sicre R, Jégou H (2016) Particular object retrieval with integral max-pooling of CNN activations. In: 4th international conference on learning representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings – reference: WeiXSLuoJHWuJZhouZHSelective convolutional descriptor aggregation for fine-Grained image retrievalIEEE Trans Image Process201726628682881364867610.1109/TIP.2017.2688133 – reference: Jegou H, Douze M, Schmid C, Pérez P (2010) Aggregating local descriptors into a compact image representation. In: 2010 IEEE computer society conference on computer vision and pattern recognition, pp 3304–3311 – reference: GaoWZhuYZhangWZhangKGaoHA hierarchical recurrent approach to predict scene graphs from a visual-attention-oriented perspectiveComput Intell2019353496516399649510.1111/coin.12202 – reference: Arandjelovic R, Zisserman A (2013) All About VLAD. In: 2013 IEEE conference on computer vision and pattern recognition, pp 1578–1585 – reference: ShenXLinZBrandtJWuYSpatially-Constrained similarity measurefor large-Scale object retrievalIEEE Trans Pattern Anal Mach Intell20143661229124110.1109/TPAMI.2013.237 – reference: JegouHPerronninFDouzeMSánchezJPérezPSchmidCAggregating local image descriptors into compact codesIEEE Trans Pattern Anal Mach Intell20123491704171610.1109/TPAMI.2011.235 – reference: LoweDGDistinctive image features from scale-Invariant keypointsInt J Comput Vis20046029111010.1023/B:VISI.0000029664.99615.94 – reference: Sattler T, Havlena M, Schindler K, Pollefeys M (2016) Large-scale location recognition and the geometric burstiness problem. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 1582–1590 . https://doi.org/10.1109/CVPR.2016.175 – reference: Nister D, Stewenius H (2006) Scalable recognition with a vocabulary tree. In: 2006 IEEE Computer society conference on computer vision and pattern recognition (CVPR’06), vol 2, pp 2161–2168 – reference: Razavian AS, Sullivan J, Carlsson S, Maki A (2016) Visual instance retrieval with deep convolutional networks. arXiv:14126574 [cs] – reference: LiQPengQYanCMultiple VLAD encoding of CNNs for image classificationComput Sci Eng2018202526310.1109/MCSE.2018.108164530 – reference: Razavian AS, Azizpour H, Sullivan J, Carlsson S (2014) CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the 2014 IEEE conference on computer vision and pattern recognition workshops, IEEE Computer Society, USA, CVPRW ’14, pp 512–519 – reference: Delhumeau J, Gosselin PH, Jégou H, Pérez P (2013) Revisiting the VLAD image representation, ACM, New York – reference: MikolajczykKSchmidCScale & affine invariant interest point detectorsInt J Comput Vision2004601638610.1023/B:VISI.0000027790.02288.f2 – reference: Babenko A, Lempitsky V (2015) Aggregating local deep features for image retrieval. In: Proceedings of the 2015 IEEE international conference on computer vision (ICCV), IEEE Computer Society, USA, ICCV ’15, pp 1269–1277 – reference: Jegou H, Zisserman A (2014) Triangulation embedding and democratic aggregation for image search. In: Proc IEEE Conf computer vision and patter recognition, pp 3310–3317 – reference: LiuPMiaoZGuoHWangYAiNAdding spatial distribution clue to aggregated vector in image retrievalEURASIP J Image Video Process201820181910.1186/s13640-018-0247-0 – reference: Csurka G, Dance CR, Fan L, Willamowski J, Bray C (2004) Visual categorization with bags of keypoints. In: In workshop on statistical learning in computer vision, ECCV, pp 1–22 – reference: Zhao WL, Gravier G, Jégou H (2013) Oriented pooling for dense and non-dense rotation-invariant features. In: Burghardt T, Damen D, Mayol-Cuevas WW, Mirmehdi M (eds) British Machine Vision Conference, BMVC 2013, Bristol, UK, September 9-13, 2013, BMVA Press – reference: Sivic J, Zisserman A (2003) Video Google: a text retrieval approach to object matching in videos. In: Proceedings Ninth IEEE international conference on computer vision, vol 2, pp 1470–1477 – reference: ZhouQWangCLiuPLiQWangYChenSDistribution entropy boosted VLAD for image retrievalEntropy201618831110.3390/e18080311 – reference: Jegou H, Douze M, Schmid C (2008) Hamming embedding and weak geometric consistency for large scale image search. In: Proceedings of the 10th European conference on computer vision: Part I, Springer, Berlin, Heidelberg, ECCV ’08, pp 304–317 – reference: Wang J, Yang J, Yu K, Lv F, Huang T, Gong Y (2010) Locality-constrained Linear Coding for image classification. In: 2010 IEEE computer society conference on computer vision and pattern recognition, pp 3360–3367 – reference: ArandjelovićRGronatPToriiAPajdlaTSivicJNetVLAD: CNN architecture for weakly supervised place recognitionIEEE Trans Pattern Anal Mach Intell20184061437145110.1109/TPAMI.2017.2711011conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence – reference: Ng JYH, Yang F, Davis LS (2015) Exploiting local features from deep networks for image retrieval. In: 2015 IEEE Conference on computer vision and pattern recognition workshops (CVPRW). IEEE, Boston, pp 53–61 – reference: HusainSSBoberMImproving large-Scale image retrieval through robust aggregation of local descriptorsIEEE Trans Pattern Anal Mach Intell20163991783179610.1109/TPAMI.2016.2613873 – reference: Bay H, Tuytelaars T, Gool LV (2006) SURF: speeded up robust features. In: Computer vision – ECCV 2006, Lecture Notes in Computer Science. springer, Berlin, pp 404–417 – reference: Arandjelović R, Zisserman A (2012) Three things everyone should know to improve object retrieval. In: 2012 IEEE conference on computer vision and pattern recognition, pp 2911–2918 – volume: 60 start-page: 91 issue: 2 year: 2004 ident: 10491_CR29 publication-title: Int J Comput Vis doi: 10.1023/B:VISI.0000029664.99615.94 – ident: 10491_CR47 doi: 10.1109/ICCV.2003.1238663 – ident: 10491_CR11 doi: 10.1145/2502081.2502171 – ident: 10491_CR5 doi: 10.1007/978-3-319-10590-1_38 – ident: 10491_CR20 doi: 10.1109/CVPR.2010.5540039 – volume: 20 start-page: 52 issue: 2 year: 2018 ident: 10491_CR23 publication-title: Comput Sci Eng doi: 10.1109/MCSE.2018.108164530 – volume: 75 start-page: 5533 issue: 10 year: 2016 ident: 10491_CR28 publication-title: Multimedia Tools and Applications doi: 10.1007/s11042-015-2524-6 – ident: 10491_CR44 doi: 10.3169/mta.4.251 – ident: 10491_CR57 doi: 10.1109/ICIP.2016.7532906 – ident: 10491_CR30 – ident: 10491_CR45 doi: 10.1109/CVPR.2016.175 – volume: 275 start-page: 2467 year: 2018 ident: 10491_CR49 publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.11.022 – volume: 78 start-page: 25655 issue: 18 year: 2019 ident: 10491_CR53 publication-title: Multimedia Tools and Applications doi: 10.1007/s11042-019-07771-2 – ident: 10491_CR24 – volume: 35 start-page: 496 issue: 3 year: 2019 ident: 10491_CR13 publication-title: Comput Intell doi: 10.1111/coin.12202 – ident: 10491_CR33 doi: 10.1109/CVPRW.2015.7301272 – volume: 42 start-page: 111 issue: 2 year: 1988 ident: 10491_CR6 publication-title: The American Statistician doi: 10.1080/00031305.1988.10475539 – volume: 2018 start-page: 9 issue: 1 year: 2018 ident: 10491_CR25 publication-title: EURASIP J Image Video Process doi: 10.1186/s13640-018-0247-0 – volume: 18 start-page: 311 issue: 8 year: 2016 ident: 10491_CR58 publication-title: Entropy doi: 10.3390/e18080311 – volume: 87 start-page: 316 issue: 3 year: 2010 ident: 10491_CR19 publication-title: Int J Comput Vis doi: 10.1007/s11263-009-0285-2 – volume: 34 start-page: 1704 issue: 9 year: 2012 ident: 10491_CR21 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2011.235 – ident: 10491_CR36 doi: 10.1109/ICCV.2015.19 – ident: 10491_CR48 – ident: 10491_CR38 doi: 10.1109/CVPR.2007.383266 – ident: 10491_CR3 doi: 10.1109/CVPR.2013.207 – volume: 75 start-page: 9045 issue: 15 year: 2016 ident: 10491_CR32 publication-title: Multimedia Tools and Applications doi: 10.1007/s11042-015-2819-7 – ident: 10491_CR42 doi: 10.1109/CVPR.2008.4587635 – volume: 60 start-page: 63 issue: 1 year: 2004 ident: 10491_CR31 publication-title: Int J Comput Vision doi: 10.1023/B:VISI.0000027790.02288.f2 – volume: 26 start-page: 2868 issue: 6 year: 2017 ident: 10491_CR52 publication-title: IEEE Trans Image Process doi: 10.1109/TIP.2017.2688133 – ident: 10491_CR16 doi: 10.1007/978-3-642-33709-3_55 – ident: 10491_CR51 doi: 10.1109/ICIP.2015.7351684 – volume: 26 start-page: 375 issue: 2 year: 2016 ident: 10491_CR26 publication-title: IEEE Trans Circ Syst Video Technol doi: 10.1109/TCSVT.2015.2409693 – ident: 10491_CR17 doi: 10.1109/CVPR.2014.417 – volume: 173 start-page: 1183 issue: P3 year: 2016 ident: 10491_CR27 publication-title: Neurocomput doi: 10.1016/j.neucom.2015.08.076 – ident: 10491_CR37 doi: 10.1007/978-3-319-10578-9_43 – ident: 10491_CR40 doi: 10.1007/978-3-642-15561-1_11 – ident: 10491_CR59 doi: 10.1109/VCIP.2014.7051576 – ident: 10491_CR7 doi: 10.1007/11744023_32 – ident: 10491_CR35 doi: 10.1109/ICCV.2017.374 – ident: 10491_CR54 doi: 10.1109/CVPR.2009.5206757 – ident: 10491_CR41 doi: 10.1109/CVPR.2007.383172 – ident: 10491_CR43 doi: 10.1109/CVPRW.2014.131 – ident: 10491_CR10 – ident: 10491_CR34 doi: 10.1109/CVPR.2006.264 – volume-title: Pattern recognition and machine learning information science and statistics year: 2006 ident: 10491_CR9 – volume: 36 start-page: 1229 issue: 6 year: 2014 ident: 10491_CR46 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2013.237 – ident: 10491_CR12 doi: 10.1109/ICIP.2014.7025610 – volume: 40 start-page: 1437 issue: 6 year: 2018 ident: 10491_CR2 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2017.2711011 – ident: 10491_CR4 – ident: 10491_CR39 doi: 10.1109/CVPR.2010.5540009 – ident: 10491_CR1 doi: 10.1109/CVPR.2012.6248018 – ident: 10491_CR60 doi: 10.1007/978-3-642-15555-0_11 – ident: 10491_CR22 doi: 10.1109/CVPR.2006.68 – ident: 10491_CR8 doi: 10.1007/978-3-030-34869-4_61 – ident: 10491_CR50 doi: 10.1109/CVPR.2010.5540018 – ident: 10491_CR14 doi: 10.1007/978-3-319-10584-0_26 – volume: 39 start-page: 1783 issue: 9 year: 2016 ident: 10491_CR15 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2016.2613873 – volume: 237 start-page: 235 year: 2017 ident: 10491_CR55 publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.12.002 – ident: 10491_CR56 doi: 10.5244/C.27.99 – ident: 10491_CR18 doi: 10.1007/978-3-540-88682-2_24
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SubjectTerms	1166: Advances of machine learning in data analytics and visual information processing Computer Communication Networks Computer Science Data Structures and Information Theory Image management Image retrieval Measurement Multimedia Information Systems Special Purpose and Application-Based Systems Statistics
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Title	FhVLAD: Fine-grained quantization and encoding high-order descriptor statistics for scalable image retrieval
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