Learning Deep Latent Representation of Color Images Using Autoencoders for Efficient Image Retrieval
The content-based image retrieval (CBIR) systems exploit the visual information present in the images to find a suitable match similar to the human visual saliency mechanism. The rise of deep learning methods accomplished notable results in learning efficient image descriptors over traditional featu...
Saved in:
| Published in: | Annual IEEE India Conference pp. 1 - 6 |
|---|---|
| Main Authors: | , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
IEEE
19.12.2024
|
| Subjects: | |
| ISSN: | 2325-9418 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | The content-based image retrieval (CBIR) systems exploit the visual information present in the images to find a suitable match similar to the human visual saliency mechanism. The rise of deep learning methods accomplished notable results in learning efficient image descriptors over traditional features. These methods automatically extract the salient features and reduce the dimension to get the useful representation of raw image data. In this light, the article proposes a feature learning framework using a deep stacked sparse autoencoder (SAE) and convolutional autoencoder (CAE) models. The autoencoder learns the meaningful approximation of the raw image data following unsupervised training. The study shows the impact of unsupervised training of autoencoder networks on the efficacy of the learned latent features using large-size CIFAR-10 and CIFAR-100 databases. Different model architectures are evaluated to find a suitable architecture for efficient feature extraction of natural color images. The proposed autoencoder networks achieve a nearly 90% reduction in size while maintaining accuracy with a simple distance-based approach. Further, the efficacy of the learned features is tested with two classifier-based retrieval methods using a trained softmax classifier. The experimental evaluation shows that the proposed method SAE and CAE model achieves promising improvements and highly competitive retrieval performance with a large-size CIFAR-10 database. |
|---|---|
| AbstractList | The content-based image retrieval (CBIR) systems exploit the visual information present in the images to find a suitable match similar to the human visual saliency mechanism. The rise of deep learning methods accomplished notable results in learning efficient image descriptors over traditional features. These methods automatically extract the salient features and reduce the dimension to get the useful representation of raw image data. In this light, the article proposes a feature learning framework using a deep stacked sparse autoencoder (SAE) and convolutional autoencoder (CAE) models. The autoencoder learns the meaningful approximation of the raw image data following unsupervised training. The study shows the impact of unsupervised training of autoencoder networks on the efficacy of the learned latent features using large-size CIFAR-10 and CIFAR-100 databases. Different model architectures are evaluated to find a suitable architecture for efficient feature extraction of natural color images. The proposed autoencoder networks achieve a nearly 90% reduction in size while maintaining accuracy with a simple distance-based approach. Further, the efficacy of the learned features is tested with two classifier-based retrieval methods using a trained softmax classifier. The experimental evaluation shows that the proposed method SAE and CAE model achieves promising improvements and highly competitive retrieval performance with a large-size CIFAR-10 database. |
| Author | Mukhopadhyay, Sudipta Kale, Mandar |
| Author_xml | – sequence: 1 givenname: Mandar surname: Kale fullname: Kale, Mandar email: mandar9975@gmail.com organization: Indian Institute of Technology Kharagpur,Electronics & Electrical Communication Engineering,Kharagpur,India – sequence: 2 givenname: Sudipta surname: Mukhopadhyay fullname: Mukhopadhyay, Sudipta email: smukho@ece.iitkgp.ac.in organization: Indian Institute of Technology Kharagpur,Electronics & Electrical Communication Engineering,Kharagpur,India |
| BookMark | eNo1UEtLAzEYjKJgrf0HHoL3rXlsNsmxbKsuLC1IPZfs5kuJtElJVsF_79bHaYZ5HeYWXYUYAKEHSuaUEv3YrJdNvVlXXGoyZ4SV81EVigl-gWZaasUF4ZoyxS7RhHEmCl1SdYNmOb8TQhghlIpygmwLJgUf9ngJcMKtGSAM-BVOCfLIzOBjwNHhOh5iws3R7CHjt3wuLD6GCKGPFlLGbnRXzvnen_s_uXFlSB4-zeEOXTtzyDD7wynaPq229UvRbp6betEWXvOhYEyDsswyBrIStuythLJzqued4cr0BDhILWmlCOmkZc4A8E52lRKVsWNsiu5_Zz0A7E7JH0362v3_wr8BbYVbuw |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/INDICON63790.2024.10958253 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISBN | 9798350391282 |
| EISSN | 2325-9418 |
| EndPage | 6 |
| ExternalDocumentID | 10958253 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IF 6IL 6IN AAWTH ABLEC ACGFS ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK OCL RIE RIL |
| ID | FETCH-LOGICAL-i93t-229e8d2d22e765d4cd7e4bf8c3ba38ac0e3e79716800b7d2faee3b7b6856ad8c3 |
| IEDL.DBID | RIE |
| IngestDate | Wed Apr 30 05:50:36 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i93t-229e8d2d22e765d4cd7e4bf8c3ba38ac0e3e79716800b7d2faee3b7b6856ad8c3 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_10958253 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-Dec.-19 |
| PublicationDateYYYYMMDD | 2024-12-19 |
| PublicationDate_xml | – month: 12 year: 2024 text: 2024-Dec.-19 day: 19 |
| PublicationDecade | 2020 |
| PublicationTitle | Annual IEEE India Conference |
| PublicationTitleAbbrev | INDICON |
| PublicationYear | 2024 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0002001154 |
| Score | 1.8927283 |
| Snippet | The content-based image retrieval (CBIR) systems exploit the visual information present in the images to find a suitable match similar to the human visual... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | Autoencoders CIFAR-10 CIFAR-100 Color Deep Autoencoder Deep learning Feature extraction Fuzzy Class membership Image retrieval Performance analysis Representation learning Retrieval Training Visualization |
| Title | Learning Deep Latent Representation of Color Images Using Autoencoders for Efficient Image Retrieval |
| URI | https://ieeexplore.ieee.org/document/10958253 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09T8MwELVoxQALX0V8ywNrSmo7sT2ifohKKFSoQ7cqti-oAwlqU34_ZzctMDCwRdHFiuzYfue8946Qe4cQPBc9iCQ3JhLWsigXTkYy1spAzI0AEYpNyCxTs5meNGL1oIUBgEA-g66_DP_yXWXX_qgMZ7hOMKPhLdKSMt2ItXYHKmxjLdMYi2LowzgbjPsvWcqljjETZKK7beBXKZWwk4yO_vkOx6Tzrcmjk91uc0L2oDwlhz_sBM-Ia8xS3-gA4IM-I4wsa_oauK6NxKikVUH7uOAt6fgdl5IVDZwB-riuK29p6WnNFHEsHQZrCf98iMNWfOUt_Cw7ZDoaTvtPUVNFIVpoXkeMaVCOOcZApokT1kkQplCWm5yr3MbAQXofKUSORjpW5ADcSJOqJM0dhp2TdlmVcEGo4A4nbC9RDnGMdlph8iMsIjKXK1ZofUk6vr_mHxufjPm2q67-uH9NDvyoeHJIT9-Qdr1cwy3Zt5_1YrW8C6P7BXKYpkY |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PT8IwFG4UTdSLvzD-tgevw9F2tD0ahLCIkxgO3Mi6vhkODgLDv9_XMlAPHrwty1uztGv7ve77vkfIvUUInoomBJIbE4gsY0EqrAxkqJWBkBsBwhebkEmiRiM9qMTqXgsDAJ58Bg136f_l22m2dEdlOMN1hBkN3yY7kRAsXMm1NkcqbGUuU1mLYvBDnDzF7dekxaUOMRdkorFu4lcxFb-XdA__-RZHpP6tyqODzX5zTLagOCEHPwwFT4mt7FLf6RPAjPYRSBYlffNs10pkVNBpTtu45M1p_IGLyYJ61gB9XJZTZ2rpiM0UkSzteHMJ97yPw1Zc7S38MOtk2O0M272gqqMQTDQvA8Y0KMssYyBbkRWZlSBMrjJuUq7SLAQO0jlJIXY00rI8BeBGmpaKWqnFsDNSK6YFnBMquMUp24yURSSjrVaY_ogMMZlNFcu1viB111_j2copY7zuqss_7t-Rvd7wpT_ux8nzFdl3I-SoIk19TWrlfAk3ZDf7LCeL-a0f6S9GmamN |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=Annual+IEEE+India+Conference&rft.atitle=Learning+Deep+Latent+Representation+of+Color+Images+Using+Autoencoders+for+Efficient+Image+Retrieval&rft.au=Kale%2C+Mandar&rft.au=Mukhopadhyay%2C+Sudipta&rft.date=2024-12-19&rft.pub=IEEE&rft.eissn=2325-9418&rft.spage=1&rft.epage=6&rft_id=info:doi/10.1109%2FINDICON63790.2024.10958253&rft.externalDocID=10958253 |