DeepIndex for accurate and efficient image retrieval

ICMR 2015 - Proceedings of the 2015 ACM International Conference on Multimedia Retrieval

Volumn , Issue , 2015, Pages 43-50

  Liu, Yu ^a   Guo, Yanming ^a   Wu, Song ^a   Lew, Michael S ^a  

^a LEIDEN UNIVERSITY   (Netherlands)

Author keywords

Bag of deep features; Convolutional neural networks; DeepIndex; Image retrieval

Indexed keywords

EID: 84962446665     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2671188.2749300     Document Type: Conference Paper

References (35)

1. P. Agrawal, R. Girshick, and J. Malik. Analyzing the performance of multilayer neural networks for object recognition. In ECCV, 2014.
2. R. Arandjelovic and A. Zisserman. Three things everyone should know to improve object retrieval. In CVPR, 2012.
3. A. Babenko and V. S. Lempitsky. The inverted multi-index. In CVPR, 2012.
4. A. Babenko, A. Slesarev, A. Chigorin, and V. S. Lempitsky. Neural codes for image retrieval. In ECCV, 2014.
5. Y. Bengio, A. C. Courville, and P. Vincent. Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives. CoRR, abs/1206.5538, 2012.
6. K. Chatfield, R. Arandjelovic, O. M. Parkhi, and A. Zisserman. On-the-fly learning for visual search of large-scale image and video datasets. IJMIR, 4(2), 2015.
7. L. Cun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel. Handwritten digit recognition with a back-propagation network. In NIPS, 1990.
8. J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. Decaf: A deep convolutional activation feature for generic visual recognition. In ICML, 2014.
9. R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014.
10. Y. Gong, L. Wang, R. Guo, and S. Lazebnik. Multi-scale orderless pooling of deep convolutional activation features. In ECCV, 2014.
11. H. Jegou, M. Douze, and C. Schmid. Hamming embedding and weak geometric consistency for large scale image search. In ECCV, 2008.
12. H. Jégou, M. Douze, and C. Schmid. On the burstiness of visual elements. In CVPR, 2009.
13. H. Jégou, M. Douze, and C. Schmid. Improving bag-of-features for large scale image search. IJCV, 87(3):316-336, 2010.
14. H. Jégou, M. Douze, C. Schmid, and P. Pérez. Aggregating local descriptors into a compact image representation. In CVPR, 2010.
15. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional Architecture for Fast Feature Embedding. In ACM Multimedia, 2014.
16. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, 2012.
17. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR, 2006.
18. M. S. Lew, N. Sebe, C. Djeraba, and R. Jain. Content-based multimedia information retrieval: State of the art and challenges. TOMC CAP, 2(1):1-19, 2006.
19. D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004.
20. M. Muja and D. G. Lowe. Fast approximate nearest neighbors with automatic algorithm configuration. In International Conference on Computer Vision Theory and Application, 2009.
21. D. Nister and H. Stewenius. Scalable recognition with a vocabulary tree. In CVPR, 2006.
22. J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman. Object retrieval with large vocabularies and fast spatial matching. In CVPR, 2007.
23. J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman. Lost in quantization: Improving particular object retrieval in large scale image databases. In CVPR, 2008.
24. A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson. CNN features off-the-shelf: an astounding baseline for recognition. In CVPR, DeepVision workshop, 2014.
25. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. ImageNet Large Scale Visual Recognition Challenge. arXiv:1409.0575, 2014.
26. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556, 2014.
27. J. Sivic and A. Zisserman. Video Google: A text retrieval approach to object matching in videos. In ICCV, 2003.
28. S. Sun, W. Zhou, H. Li, and Q. Tian. Search by detection: Object-level feature for image retrieval. In ICIMCS, 2014.
29. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. CoRR, abs/1409.4842, 2014.
30. G. Tolias, Y. Avrithis, and H. Jégou. To aggregate or not to aggregate: selective match kernels for image search. In ICCV, 2013.
31. J. Wan, D. Wang, S. C. H. Hoi, P. Wu, J. Zhu, Y. Zhang, and J. Li. Deep learning for content-based image retrieval: A comprehensive study. In ACM Multimedia, 2014.
32. C. Wengert, M. Douze, and H. Jégou. Bag-of-colors for improved image search. In ACM Multimedia, 2011.
33. M. Yang, X. Wang, Y. Lin, and Q. Tian. Semantic-aware co-indexing for near-duplicate image retrieval. In ICCV, 2014.
34. L. Zheng, S. Wang, F. He, and Q. Tian. Seeing the big picture: Deep embedding with contextual evidences. CoRR, abs/1406.0132, 2014.
35. L. Zheng, S. Wang, Z. Liu, and Q. Tian. Packing and padding: Coupled multi-index for accurate image retrieval. In CVPR, 2014.