nach oben

International Journal of Computer Vision

Erschienen in:

26.09.2020

Deep Hashing with Hash-Consistent Large Margin Proxy Embeddings

verfasst von: Pedro Morgado, Yunsheng Li, Jose Costa Pereira, Mohammad Saberian, Nuno Vasconcelos

Erschienen in: International Journal of Computer Vision | Ausgabe 2/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Image hash codes are produced by binarizing the embeddings of convolutional neural networks (CNN) trained for either classification or retrieval. While proxy embeddings achieve good performance on both tasks, they are non-trivial to binarize, due to a rotational ambiguity that encourages non-binary embeddings. The use of a fixed set of proxies (weights of the CNN classification layer) is proposed to eliminate this ambiguity, and a procedure to design proxy sets that are nearly optimal for both classification and hashing is introduced. The resulting hash-consistent large margin (HCLM) proxies are shown to encourage saturation of hashing units, thus guaranteeing a small binarization error, while producing highly discriminative hash-codes. A semantic extension (sHCLM), aimed to improve hashing performance in a transfer scenario, is also proposed. Extensive experiments show that sHCLM embeddings achieve significant improvements over state-of-the-art hashing procedures on several small and large datasets, both within and beyond the set of training classes.

Vorheriger Artikel Unsupervised Deep Representation Learning for Real-Time Tracking

Nächster Artikel LaSOT: A High-quality Large-scale Single Object Tracking Benchmark

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Akata, Z., Perronnin, F., Harchaoui, Z., & Schmid, C. (2013). Label-embedding for attribute-based classification. In IEEE conference on computer vision and pattern recognition (CVPR).

Andoni, A., & Indyk, P. (2006). Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In IEEE symposium on foundations of computer science (FOCS).

Babenko, A., Slesarev, A., Chigorin, A., & Lempitsky, V. (2014). Neural codes for image retrieval. In European conference on computer vision (ECCV).

Bach, J. R., Fuller, C., Gupta, A., Hampapur, A., Horowitz, B., Humphrey, R., Jain, R. C., & Shu, C. F. (1996). Virage image search engine: an open framework for image management. In Storage and retrieval for still image and video databases IV (Vol. 2670).

Banerjee, A., Merugu, S., Dhillon, I. S., & Ghosh, J. (2005). Clustering with bregman divergences. Journal of Machine Learning Research, 6, 1705–1749.MathSciNetMATH

Barndorff-Nielsen, O. (2014). Information and exponential families: In statistical theory. New York: Wiley. CrossRef

Bell, S., & Bala, K. (2015). Learning visual similarity for product design with convolutional neural networks. ACM Transactions on Graphics (TOG), 34(4), 1–10.CrossRef

Bregman, L. M. (1967). The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. USSR Computational Mathematics and Mathematical Physics, 7(3), 200–217. MathSciNetCrossRef

Cakir, F., He, K., Adel Bargal, S., & Sclaroff, S. (2017). Mihash: Online hashing with mutual information. In International conference on computer vision (ICCV).

Cakir, F., He, K., & Sclaroff, S. (2018). Hashing with binary matrix pursuit. In Proceedings of the European conference on computer vision (ECCV) (pp. 332–348).

Cao, Y., Long, M., Wang, J., Zhu, H., & Wen, Q. (2016) Deep quantization network for efficient image retrieval. In AAAI Conference on Artificial Intelligence.

Chopra, S., Hadsell, R., & LeCun, Y. (2005). Learning a similarity metric discriminatively, with application to face verification. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Chua, T. S., Tang, J., Hong, R., Li, H., Luo, Z., & Zheng, Y. T. (2009). NUS-WIDE: A real-world web image database from national university of Singapore. In ACM Conferrence on Image and Video Retrieval (CIVR).

Datar, M., Immorlica, N., Indyk, P., & Mirrokni, V. S. (2004). Locality-sensitive hashing scheme based on p-stable distributions. In Symposium on computational geometry (SOCG).

Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In IEEE conference on computer vision and pattern recognition (CVPR).

Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., et al. (1995). Query by image and video content: The QBIC system. Computer, 28(9), 23–32.CrossRef

Frome, A., Corrado, G. S., Shlens, J., Bengio, S., Dean, J., Mikolov, T., et al. (2013). Devise: A deep visual-semantic embedding model. In advances in neural information processing systems (NIPS).

Goldberger, J., Hinton, G. E., Roweis, S. T., & Salakhutdinov, R. R. (2005) Neighbourhood components analysis. In: Advances in neural information processing systems (NIPS).

Gong, Y., Lazebnik, S., Gordo, A., & Perronnin, F. (2013). Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 35(12), 2916–2929.CrossRef

Gordo, A., Almazán, J., Revaud, J., & Larlus, D. (2016). Deep image retrieval: Learning global representations for image search. In European conference on computer vision (ECCV)

Hadsell, R., Chopra, S., & LeCun, Y. (2006). Dimensionality reduction by learning an invariant mapping. In IEEE conference on computer vision and pattern recognition (CVPR) .

He, K., Cakir, F., Bargal, S. A., & Sclaroff, S. (2018). Hashing as tie-aware learning to rank. In IEEE conference on computer vision and pattern recognition (CVPR)

Huang, S., Xiong, Y., Zhang, Y., & Wang, J. (2017). Unsupervised triplet hashing for fast image retrieval. In Thematic workshops of ACM multimedia

Jain, H., Zepeda, J., Pérez, P., & Gribonval, R. (2017). Subic: A supervised, structured binary code for image search. In International conference on computer vision (ICCV)

Jegou, H., Douze, M., & Schmid, C. (2011). Product quantization for nearest neighbor search. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 33(1), 117–128.CrossRef

Jiang, Q. Y., & Li, W. J. (2018). Asymmetric deep supervised hashing. In Thirty-second AAAI conference on artificial intelligence.

Krizhevsky, A. (2009). Learning multiple layers of features from tiny images. University of Toronto.

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (NIPS).

Kulis, B., & Darrell, T. (2009). Learning to hash with binary reconstructive embeddings. In Advances in neural information processing systems (NIPS)

Lai, H., Pan, Y., Liu, Y., & Yan, S. (2015). Simultaneous feature learning and hash coding with deep neural networks. In IEEE conference on computer vision and pattern recognition (CVPR).

Lampert, C. H., Nickisch, H., & Harmeling, S. (2009). Learning to detect unseen object classes by between-class attribute transfer. In IEEE conference on computer vision and pattern recognition (CVPR).

Li, L., Su, H., Xing, E., & Fei-Fei, L. (2010). Object bank: A high-level image representation for scene classification and semantic feature sparsification. In Advances in neural information processing systems (NIPS).

Li, Q., Sun, Z., He, R., & Tan, T. (2017). Deep supervised discrete hashing. In Advances in neural information processing systems (NIPS).

Li, W. J., Wang, S., & Kang, W. C. (2016). Feature learning based deep supervised hashing with pairwise labels. In AAAI conference on artificial intelligence.

Lin, K., Lu, J., Chen, C. S., & Zhou, J. (2016). Learning compact binary descriptors with unsupervised deep neural networks. In IEEE conference on computer vision and pattern recognition (CVPR).

Lin, K., Yang, H. F., Hsiao, J. H., & Chen, C.S. (2015) Deep learning of binary hash codes for fast image retrieval. In IEEE conference on Computer vision and pattern recognition (workshops).

Liong, E., Lu, J., Wang, G., Moulin, P., & Zhou, J. (2015). Deep hashing for compact binary codes learning. In IEEE conference on computer vision and pattern recognition (CVPR).

Liu, H., Wang, R., Shan, S., & Chen, X. (2016). Deep supervised hashing for fast image retrieval. In IEEE conference on computer vision and pattern recognition (CVPR).

Liu, W., Wang, J., Ji, R., Jiang, Y. G., & Chang, S. F. (2012). Supervised hashing with kernels. In IEEE conference on computer vision and pattern recognition (CVPR).

Lu, J., Liong, V. E., & Zhou, J. (2017). Deep hashing for scalable image search. IEEE Transactions on Image Processing (TIP), 26(5), 2352–2367.MathSciNetCrossRef

Morgado, P., & Vasconcelos, N. (2017). Semantically consistent regularization for zero-shot recognition. In IEEE conference on computer vision and pattern recognition (CVPR).

Movshovitz-Attias, Y., Toshev, A., Leung, T. K., Ioffe, S., & Singh, S. (2017). No fuss distance metric learning using proxies. In International conference on computer vision (ICCV).

Mu, Y., & Yan, S. (2010). Non-metric locality-sensitive hashing. In AAAI conference on artificial intelligence.

Nelder, J. A., & Wedderburn, R. W. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384.CrossRef

Norouzi, M., & Blei, D. M. (2011). Minimal loss hashing for compact binary codes. In International conference on machine learning (ICML).

Oh Song, H., Xiang, Y., Jegelka, S., & Savarese, S. (2016). Deep metric learning via lifted structured feature embedding. In IEEE conference on computer vision and pattern recognition (CVPR)

Pereira, J. C., & Vasconcelos, N. (2014). Cross-modal domain adaptation for text-based regularization of image semantics in image retrieval systems. In Computer Vision and Image Understanding (Vol. 124).

Rasiwasia, N., Moreno, P., & Vasconcelos, N. (2007). Bridging the gap: Query by semantic example. IEEE Transactions on Multimedia, 9(5), 923–938.CrossRef

Rohrbach, M., Stark, M., & Schiele, B. (2011). Evaluating knowledge transfer and zero-shot learning in a large-scale setting. In IEEE conference on computer vision and pattern recognition (CVPR).

Sablayrolles, A., Douze, M., Usunier, N., & Jégou, H. (2017). How should we evaluate supervised hashing? In IEEE International conference on acoustics, speech and signal processing (ICASSP).

Schroff, F., Kalenichenko, D., & Philbin, J. Facenet (2015). A unified embedding for face recognition and clustering. In IEEE conference on computer vision and pattern recognition (CVPR).

Shen, F., Shen, C., Liu, W., & Tao Shen, H. (2015). Supervised discrete hashing. In IEEE conference on computer vision and pattern recognition (CVPR)

Smeulders, A., Worring, M., Santini, S., Gupta, A., & Jain, R. (2000). Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 22(12), 1349–1380.CrossRef

Smith, J. R., & Chang, S. F. (1997). Visualseek: A fully automated content-based image query system. In ACM international conference on multimedia.

Sohn, K. (2016). Improved deep metric learning with multi-class n-pair loss objective. In Advances in neural information processing systems (NIPS)

Song, H. O., Jegelka, S., Rathod, V., & Murphy, K. (2016). Learnable structured clustering framework for deep metric learning. arXiv preprint arXiv:1612.01213.

Sun, Y., Chen, Y., Wang, X., & Tang, X. (2014). Deep learning face representation by joint identification-verification. In Advances in neural information processing systems (NIPS).

Tammes, P. (1930). On the origin of number and arrangement of the places of exit on the surface of pollen-grains. Ph.D. thesis, University of Groningen.

Torresani, L., Szummer, M., & Fitzgibbon, A. (2010). Efficient object category recognition using classemes. In European conference on computer vision (ECCV).

Wang, J., Kumar, S., & Chang, S. F. (2010). Semi-supervised hashing for scalable image retrieval. In IEEE conference on computer vision and pattern recognition (CVPR).

Wang, J., Song, Y., Leung, T., Rosenberg, C., Wang, J., Philbin, J., Chen, B., & Wu, Y. (2014). Learning fine-grained image similarity with deep ranking. In IEEE conference on computer vision and pattern recognition (CVPR).

Wang, X., Shi, Y., & Kitani, K. M. (2016). Deep supervised hashing with triplet labels. In Asian conference on computer vision (ACCV).

Weinberger, K. Q., & Saul, L. K. (2009). Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, 10(9), 207–244.MATH

Weiss, Y., Torralba, A., & Fergus, R. (2009). Spectral hashing. In Advances in Neural Information Processing Systems (NIPS).

Wen, Y., Zhang, K., Li, Z., & Qiao, Y. (2016). A discriminative feature learning approach for deep face recognition. In European conference on computer vision (ECCV) (pp. 499–515). Berlin: Springer.

Wright, S., & Nocedal, J. (1999). Numerical optimization. Science, 35, 566.MATH

Xia, R., Pan, Y., Lai, H., Liu, C., & Yan, S. (2014). Supervised hashing for image retrieval via image representation learning. In AAAI conference on artificial intelligence

Xie, S., & Tu, Z. (2015). Holistically-nested edge detection. In: Proceedings of the IEEE international conference on computer vision (pp. 1395–1403).

Yang, H. F., Lin, K., & Chen, C. S. (2017). Supervised learning of semantics-preserving hash via deep convolutional neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(2), 437–451.CrossRef

Zhang, D., Wu, W., Cheng, H., Zhang, R., Dong, Z., & Cai, Z. (2017). Image-to-video person re-identification with temporally memorized similarity learning. IEEE Transactions on Circuits and Systems for Video Technology, 28(10), 2622–2632.CrossRef

Zhang, J., Peng, Y., & Zhang, J. (2016). Query-adaptive image retrieval by deep weighted hashing. IEEE Transactions on Multimedia.

Zhang, R., Li, J., Sun, H., Ge, Y., Luo, P., Wang, X., et al. (2019). Scan: Self-and-collaborative attention network for video person re-identification. IEEE Transactions on Image Processing, 28(10), 4870–4882.MathSciNetCrossRef

Zhang, R., Lin, L., Zhang, R., Zuo, W., & Zhang, L. (2015). Bit-scalable deep hashing with regularized similarity learning for image retrieval and person re-identification. IEEE Transactions on Image Processing, 24(12), 4766–4779.MathSciNetCrossRef

Zhao, F., Huang, Y., Wang, L., & Tan, T. (2015). Deep semantic ranking based hashing for multi-label image retrieval. In IEEE conference on computer vision and pattern recognition (CVPR).

Zhong, G., Xu, H., Yang, P., Wang, S., & Dong, J. (2016). Deep hashing learning networks. In International joint conference on neural networks (IJCNN).

Zhu, H., Long, M., Wang, J., & Cao, Y. (2016). Deep hashing network for efficient similarity retrieval. In AAAI conference on artificial intelligence.

Titel: Deep Hashing with Hash-Consistent Large Margin Proxy Embeddings
verfasst von: Pedro Morgado
Yunsheng Li
Jose Costa Pereira
Mohammad Saberian
Nuno Vasconcelos
Publikationsdatum: 26.09.2020
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 2/2021
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-020-01362-7

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 2/2021

Beyond Covariance: SICE and Kernel Based Visual Feature Representation

HOTA: A Higher Order Metric for Evaluating Multi-object Tracking

Unsupervised Deep Representation Learning for Real-Time Tracking

Unsupervised Domain Adaptation in the Wild via Disentangling Representation Learning

Binarized Neural Architecture Search for Efficient Object Recognition

Image Matching Across Wide Baselines: From Paper to Practice