Visual instance retrieval with deep convolutional networks

3rd International Conference on Learning Representations, ICLR 2015 - Workshop Track Proceedings

Volumn , Issue , 2015, Pages

  Razavian, Ali S ^a   Sullivan, Josephine ^a   Carlsson, Stefan ^a   Maki, Atsuto ^a  

^a ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

Author keywords

Convolutional network; Learning representation; Multi resolution search; Visual instance retrieval

Indexed keywords

IMAGE RETRIEVAL;

CONVOLUTIONAL NETWORKS; GEOMETRIC INVARIANCE; IMAGE REPRESENTATIONS; INSTANCE RETRIEVAL; LEARNING REPRESENTATION; MULTI-RESOLUTION SEARCH; SPATIAL CONSISTENCY; STATE-OF-THE-ART METHODS;

CONVOLUTION;

EID: 85083954456     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (39)

1. Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman, “Deep inside convolutional networks: Visualising image classification models and saliency maps.,” arXiv:1312.6034 [cs.CV], 2013.
2. Ross B. Girshick, Forrest N. Iandola, Trevor Darrell, and Jitendra Malik, “Deformable part models are convolutional neural networks,” arXiv:1409.5403 [cs.CV], 2014.
3. Ken Chatfield, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman, “Return of the devil in the details: Delving deep into convolutional nets,” arXiv:1405.3531 [cs.CV], 2014.
4. Matthew D. Zeiler and Rob Fergus, “Visualizing and understanding convolutional networks,” in ECCV, 2014, pp. 818–833.
5. Hossein Azizpour, Ali S. Razavian, Josephine Sullivan, Atsuto Maki, and Stefan Carlsson, “Factors of transferability for a generic ConvNet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016.
6. Ali S. Razavian, Josephine Sullivan, Atsuto Maki, and Stefan Carlsson, “Visual instance retrieval with deep convolutional networks,” arxiv:1412.6574 [cs.CV], 2014.
7. Maxime Oquab, Léon Bottou, Ivan Laptev, and Josef Sivic, “Learning and transferring mid-level image representations using convolutional neural networks,” in CVPR, 2014.
8. Jeff Donahue, Yangqing Jia, Oriol Vinyals, Judy Hoffman, Ning Zhang, Eric Tzeng, and Trevor Darrell, “Decaf: A deep convolutional activation feature for generic visual recognition,” in ICML, 2014.
9. Philipp Fischer, Alexey Dosovitskiy, and Thomas Brox, “Descriptor matching with convolutional neural networks: a comparison to SIFT,” arXiv:1405.5769 [cs.CV], 2014.
10. Krystian Mikolajczyk and Cordelia Schmid, “A Performance Evaluation of Local Descriptors,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005.
11. Ali S. Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson, “CNN features off-the-shelf: an astounding baseline for recognition,” CVPRW, 2014.
12. Relja Arandjelovic, Petr Gronat, Akihiko Torii, Tomas Pajdla, and Josef Sivic, “NetVLAD: CNN architecture for weakly supervised place recognition,” arxiv:1511.07247 [cs.CV], 2015.
13. Artem Babenko, Anton Slesarev, Alexander Chigorin, and Victor S. Lempitsky, “Neural codes for image retrieval,” in ECCV, 2014.
14. Ji Wan, Dayong Wang, Steven Chu Hong Hoi, Pengcheng Wu, Jianke Zhu, Yong-dong Zhang, and Jintao Li, “Deep learning for content-based image retrieval: A comprehensive study,” in ACM Multimedia, 2014, pp. 157–166.
15. Eleftherios Spyromitros-Xioufis, Symeon Papadopoulos, Ioannis (Yiannis) Kompatsiaris, Grigorios Tsoumakas, and Ioannis Vlahavas, “A Comprehensive Study Over VLAD and Product Quantization in Large-Scale Image Retrieval,” in IEEE Transactions on Multimedia, 2014, pp. 1713–1728.
16. Yunchao Gong, Liwei Wang, Ruiqi Guo, and Svetlana Lazebnik, “Multi-scale orderless pooling of deep convolutional activation features,” in ECCV, 2014, pp. 392–407.
17. Ondrej Chum, James Philbin, Josef Sivic, Micheal Isard, and Andrew Zisserman, “Total recall: Automatic query expansion with a generative feature model for object retrieval,” in ICCV, 2007.
18. James Philbin, Ondrej Chum, Michael Isard, Josef Sivic, and Andrew Zisserman, “Object retrieval with large vocabularies and fast spatial matching,” in CVPR, 2007.
19. Michal Perdoch, Ondrej Chum, and Jiri Matas, “Efficient representation of local geometry for large scale object retrieval,” in CVPR, 2009.
20. Alex Krizhevsky and Geoffrey E. Hinton, “Using very deep autoencoders for content-based image retrieval,” in Proceedings of the European Symposium of Artifical Neural Networks (ESANN), 2011.
21. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, “ImageNet classification with deep convolutional neural networks,” in NIPS, 2012.
22. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A Large-Scale Hierarchical Image Database,” in CVPR, 2009.
23. Joe Yue-Hei Ng, Fan Yang, and Larry S. Davis, “Exploiting local features from deep networks for image retrieval,” CVPRW, 2015.
24. Josef Sivic and Andrew Zisserman, “Video google: A text retrieval approach to object matching in videos,” in ICCV, 2003.
25. Florent Perronnin and Christopher R. Dance, “Fisher kernels on visual vocabularies for image categorization,” in CVPR, 2007.
26. Hervé Jégou, Florent Perronnin, Matthijs Douze, Jorge Sánchez, Patrick Pérez, and Cordelia Schmid, “Aggregating local image descriptors into compact codes,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 9, pp. 1704–1716, 2012.
27. Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman, “Learning local feature descriptors using convex optimisation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 8, pp. 1573–1585, 2014.
28. Giorgos Tolias, Yannis S. Avrithis, and Hervé Jégou, “To aggregate or not to aggregate: Selective match kernels for image search,” in ICCV, 2013.
29. Artem Babenko, and Victor Lempitsky, “Aggregating Local Deep Features for Image Retrieval” in ICCV, 2015.
30. Hervé Jégou and Andrew Zisserman, “Triangulation embedding and democratic aggregation for image search,” in CVPR, 2014.
31. Bolei Zhou, Aditya Khosla, Àgata Lapedriza, Aude Oliva, and Antonio Torralba, “Object detectors emerge in deep scene cnns,” in ICLR, 2015.
32. Antonio Torralba, Rob Fergus, and Yair Weiss, “Small codes and large image databases for recognition” in CVPR, 2008.
33. Ran Tao, Efstratios Gavves, Cees G. M. Snoek, and Arnold W. M. Smeulders, “Locality in generic instance search from one example,” in CVPR, 2014, pp. 2099–2106.
34. James Philbin, Ondrej Chum, Michael Isard, Josef Sivic, and Andrew Zisserman, “Lost in quantization: Improving particular object retrieval in large scale image databases,” in CVPR, 2008.
35. Relja Arandjelović and Andrew Zisserman, “Smooth object retrieval using a bag of boundaries,” in ICCV, 2011.
36. Hervé Jégou, Matthijs Douze, and Cordelia Schmid, “Hamming embedding and weak geometric consistency for large scale image search,” in ECCV, 2008.
37. David Nistér and Henrik Stewénius, “Scalable recognition with a vocabulary tree,” in CVPR, 2006.
38. Wan-Lei Zhao, Hervé Jégou, Guillaume Gravier, et al., “Oriented pooling for dense and non-dense rotation-invariant features,” in BMVC, 2013.
39. Karen Simonyan and Andrew Zisserman, “Very deep convolutional networks for large-scale image recognition,” in ICLR, 2015.