Multiple-Instance Discriminant Analysis for Weakly Supervised Segment Annotation

In this paper, we propose a multiple-instance discriminant analysis algorithm for weakly supervised segment annotation. We introduce a selection parameter for each image/video with weak labels and expect that it can sift out object regions from the background clutter to train a better transformation...

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Vydáno v:	IEEE transactions on image processing Ročník 28; číslo 11; s. 5716 - 5728
Hlavní autoři:	Wang, Liantao, Li, Qingwu, Zhou, Yan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.11.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms Annotations Clutter Discriminant analysis Eigenvalues Image segmentation linear discriminant analysis Linear programming Machine learning Multiple-instance learning Optimization Parameters Proposals Quadratic programming region selection Regularization Semantics Support vector machines Training Transformations Visualization weakly supervised segment annotation
ISSN:	1057-7149, 1941-0042, 1941-0042
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Abstract	In this paper, we propose a multiple-instance discriminant analysis algorithm for weakly supervised segment annotation. We introduce a selection parameter for each image/video with weak labels and expect that it can sift out object regions from the background clutter to train a better transformation vector. The selection parameter and the transformation parameter are incorporated into a single objective function and optimized in an alternate way. The optimization is an iteration between the eigenvalue decomposition and a set of quadratic programming. We also integrate a regularization term into the objective function to formulate the spatial constraint of segments, which is ignored in ordinary multiple-instance learning methods. The algorithm is able to overcome the limitations that arise when applying ordinary multiple-instance methods to the task. The experimental results validate the effectiveness of our method.
AbstractList	In this paper, we propose a multiple-instance discriminant analysis algorithm for weakly supervised segment annotation. We introduce selection parameter for each image/video with weak labels, and expect it can sift out object regions from the background clutter to train a better transformation vector. The selection parameter and the transformation parameter are incorporated into a single objective function, and optimized in an alternate way. The optimization is an iteration between eigenvalue decomposition and a set of quadratic programming. We also integrate a regularization term into the objective function to formulate the spatial constraint of segments, which is ignored in ordinary multiple-instance learning methods. The algorithm is able to overcome the limitations that arise when applying ordinary multiple-instance methods to the task. Experimental results validate the effectiveness of our method. In this paper, we propose a multiple-instance discriminant analysis algorithm for weakly supervised segment annotation. We introduce a selection parameter for each image/video with weak labels and expect that it can sift out object regions from the background clutter to train a better transformation vector. The selection parameter and the transformation parameter are incorporated into a single objective function and optimized in an alternate way. The optimization is an iteration between the eigenvalue decomposition and a set of quadratic programming. We also integrate a regularization term into the objective function to formulate the spatial constraint of segments, which is ignored in ordinary multiple-instance learning methods. The algorithm is able to overcome the limitations that arise when applying ordinary multiple-instance methods to the task. The experimental results validate the effectiveness of our method. In this paper, we propose a multiple-instance discriminant analysis algorithm for weakly supervised segment annotation. We introduce a selection parameter for each image/video with weak labels and expect that it can sift out object regions from the background clutter to train a better transformation vector. The selection parameter and the transformation parameter are incorporated into a single objective function and optimized in an alternate way. The optimization is an iteration between the eigenvalue decomposition and a set of quadratic programming. We also integrate a regularization term into the objective function to formulate the spatial constraint of segments, which is ignored in ordinary multiple-instance learning methods. The algorithm is able to overcome the limitations that arise when applying ordinary multiple-instance methods to the task. The experimental results validate the effectiveness of our method.In this paper, we propose a multiple-instance discriminant analysis algorithm for weakly supervised segment annotation. We introduce a selection parameter for each image/video with weak labels and expect that it can sift out object regions from the background clutter to train a better transformation vector. The selection parameter and the transformation parameter are incorporated into a single objective function and optimized in an alternate way. The optimization is an iteration between the eigenvalue decomposition and a set of quadratic programming. We also integrate a regularization term into the objective function to formulate the spatial constraint of segments, which is ignored in ordinary multiple-instance learning methods. The algorithm is able to overcome the limitations that arise when applying ordinary multiple-instance methods to the task. The experimental results validate the effectiveness of our method.
Author	Yan Zhou Liantao Wang Qingwu Li
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References	ref57 ref13 ref56 ref12 ref59 ref15 ref58 ref14 yan (ref39) 2012; 13 ref52 ref55 ref10 ref17 ref19 ye (ref22) 2008; 9 ref51 ref50 fukunaga (ref53) 1990 bishop (ref54) 2006 ref46 ref45 ref48 ref47 ref42 ref44 ref43 zhang (ref6) 2016 chen (ref24) 2012 ref49 jia (ref41) 2009; 20 ref9 ref3 zhu (ref4) 2014 ref5 ref40 viola (ref8) 2005 ref35 liu (ref62) 2012 ref34 ref37 ref31 ref30 ref33 ref32 ref2 ref1 ref38 sugiyama (ref36) 2007; 8 simonyan (ref65) 2014; abs 1409 1556 doersch (ref7) 2013 ref67 ref26 ref25 ref64 ref20 ref63 ref66 ref21 andrews (ref16) 2002 ref28 ref27 ref29 ren (ref11) 2015 yakhnenko (ref18) 2011 ref60 zhou (ref23) 2012 ref61
References_xml	– ident: ref64 doi: 10.1007/978-3-319-10599-4_28 – ident: ref57 doi: 10.1109/TPAMI.2010.161 – start-page: 1 year: 2011 ident: ref18 article-title: Region-based image classification with a latent SVM model – start-page: 91 year: 2015 ident: ref11 article-title: Faster R-CNN: Towards real-time object detection with region proposal networks publication-title: Proc Adv Neural Inf Process Syst – ident: ref38 doi: 10.1109/TNN.2011.2156808 – ident: ref15 doi: 10.1109/ICCV.2017.204 – ident: ref3 doi: 10.1109/CVPR.2013.321 – ident: ref42 doi: 10.1016/j.patcog.2014.02.002 – ident: ref13 doi: 10.1109/CVPR.2016.319 – ident: ref14 doi: 10.1109/CVPR.2016.326 – ident: ref21 doi: 10.1109/ICCV.2007.4408856 – ident: ref37 doi: 10.1007/s10994-009-5125-7 – ident: ref2 doi: 10.1109/CVPR.2011.5995545 – ident: ref60 doi: 10.1007/978-3-642-33709-3_1 – ident: ref47 doi: 10.1109/TPAMI.2016.2567393 – ident: ref50 doi: 10.1016/j.patcog.2016.01.015 – ident: ref20 doi: 10.1007/s10115-006-0013-y – ident: ref56 doi: 10.1016/j.neucom.2015.10.066 – ident: ref46 doi: 10.1109/ICCV.2015.352 – ident: ref67 doi: 10.1109/TPAMI.2009.144 – ident: ref25 doi: 10.1109/TCYB.2017.2647965 – start-page: 71 year: 2012 ident: ref23 article-title: Arbitrary-shape object localization using adaptive image grids publication-title: Proc Asian Conf Comput Vis (ACCV) – ident: ref35 doi: 10.1109/NNSP.1999.788121 – ident: ref52 doi: 10.1007/978-3-030-01240-3_23 – ident: ref61 doi: 10.1007/978-3-642-33783-3_45 – ident: ref44 doi: 10.5244/C.28.6 – ident: ref51 doi: 10.1109/CVPR.2018.00399 – volume: 8 start-page: 985 year: 2007 ident: ref36 article-title: Covariate shift adaptation by importance weighted cross validation publication-title: J Mach Learn Res – ident: ref30 doi: 10.1007/978-3-540-88682-2_16 – ident: ref66 doi: 10.1109/CVPR.2008.4587586 – ident: ref43 doi: 10.1023/B:VISI.0000029664.99615.94 – volume: 13 start-page: 607 year: 2012 ident: ref39 article-title: Non-sparse multiple kernel Fisher discriminant analysis publication-title: J Mach Learn Res – start-page: 577 year: 2002 ident: ref16 article-title: Support vector machines for multiple-instance learning publication-title: Proc 15th Int Conf Neural Inf Proc – ident: ref17 doi: 10.1109/TPAMI.2009.167 – ident: ref9 doi: 10.21236/ADA507101 – start-page: 1125 year: 2014 ident: ref4 article-title: Learning from weakly supervised data by the expectation loss SVM (e-SVM) algorithm publication-title: Proc Adv Neural Inf Process Syst – ident: ref31 doi: 10.1109/CVPR.2015.7298968 – ident: ref10 doi: 10.1109/TPAMI.2016.2535231 – ident: ref29 doi: 10.1109/CVPR.2014.309 – ident: ref49 doi: 10.1007/978-3-319-46493-0_42 – start-page: 253 year: 2012 ident: ref62 article-title: Key instance detection in multi-instance learning publication-title: Proc Asian Conf Mach Learn – ident: ref1 doi: 10.1007/978-3-642-04174-7_2 – year: 1990 ident: ref53 publication-title: Introduction to statistical pattern recognition – ident: ref5 doi: 10.1109/TIP.2016.2514503 – ident: ref12 doi: 10.1109/CVPR.2016.311 – start-page: 1 year: 2005 ident: ref8 article-title: Multiple instance boosting for object detection publication-title: Proc Adv Neural Inf Process Syst – start-page: 1274 year: 2012 ident: ref24 article-title: Efficient activity detection with max-subgraph search publication-title: Proc IEEE Conf Comput Vis Pattern Recognit – ident: ref63 doi: 10.1109/CVPR.2015.7298711 – volume: 9 start-page: 719 year: 2008 ident: ref22 article-title: Multi-class discriminant kernel learning via convex programming publication-title: J Mach Learn Res – ident: ref32 doi: 10.1109/ICCV.2015.75 – ident: ref19 doi: 10.1023/A:1018677409366 – start-page: 494 year: 2013 ident: ref7 article-title: Mid-level visual element discovery as discriminative mode seeking publication-title: Proc 27th Annu Conf Adv Neural Inf Process Syst – ident: ref34 doi: 10.1111/j.1469-1809.1936.tb02137.x – ident: ref27 doi: 10.1109/ICCV.2013.271 – ident: ref48 doi: 10.1109/TIP.2017.2688133 – ident: ref28 doi: 10.1109/TCYB.2016.2631528 – ident: ref45 doi: 10.1109/CVPR.2015.7298642 – ident: ref40 doi: 10.1109/TPAMI.2015.2435740 – volume: 20 start-page: 729 year: 2009 ident: ref41 article-title: Trace ratio problem revisited publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2009.2015760 – ident: ref58 doi: 10.1007/978-3-642-15561-1_33 – year: 2006 ident: ref54 publication-title: Pattern Recognition and Machine Learning – volume: abs 1409 1556 year: 2014 ident: ref65 article-title: Very deep convolutional networks for large-scale image recognition publication-title: CoRR – start-page: 3538 year: 2016 ident: ref6 article-title: Bridging saliency detection to weakly supervised object detection based on self-paced curriculum learning publication-title: Proc Intern Joint Conf Artificial Intel (IJCAI) – ident: ref33 doi: 10.5244/C.31.29 – ident: ref26 doi: 10.1109/CVPR.2006.91 – ident: ref59 doi: 10.1109/ICCV.2011.6126383 – ident: ref55 doi: 10.1109/ICPR.2014.29
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SubjectTerms	Algorithms Annotations Clutter Discriminant analysis Eigenvalues Image segmentation linear discriminant analysis Linear programming Machine learning Multiple-instance learning Optimization Parameters Proposals Quadratic programming region selection Regularization Semantics Support vector machines Training Transformations Visualization weakly supervised segment annotation
Title	Multiple-Instance Discriminant Analysis for Weakly Supervised Segment Annotation
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