Efficient On-The-fly category retrieval using ConvNets and GPUs

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9003, Issue , 2015, Pages 129-145

  Chatfield, Ken ^a   Simonyan, Karen ^a   Zisserman, Andrew ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; CLASSIFICATION (OF INFORMATION); CLASSIFIERS; EMBEDDED SYSTEMS; IMAGE CLASSIFICATION; PROGRAM PROCESSORS;

BENCHMARK DATASETS; CONVOLUTIONAL NETWORKS; LARGE-SCALE DATASETS; LEARNING AND RANKINGS; MEMORY REQUIREMENTS; PRODUCT QUANTIZATIONS; SIMULTANEOUS TRAINING; STATE-OF-THE-ART PERFORMANCE;

COMPUTER VISION;

EID: 84938855688     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-16865-4_9     Document Type: Conference Paper

References (34)

1. Deng, J, Dong, W, Socher, R, Li, L.J, Li, K, Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: Proceedings of CVPR (2009)
2. Soomro, K, Zamir, A.R, Shah, M.: UCF101: a dataset of 101 human actions classes from videos in the wild. CoRR abs/1212.0402 (2012)
3. Sivic, J, Zisserman, A.: Video Google: a text retrieval approach to object matching in videos. In: Proceedings of ICCV, vol. 2, pp. 1470–1477 (2003)
4. Nister, D, Stewenius, H.: Scalable recognition with a vocabulary tree. In: Proceedings of CVPR, pp. 2161–2168 (2006)
5. Philbin, J, Chum, O, Isard, M, Sivic, J, Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: Proceedings of CVPR (2007)
6. Jegou, H, Douze, M, Schmid, C.: Hamming embedding and weak geometric consistency for large scale image search. In: Forsyth, D, Torr, P, Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 304–317. Springer, Heidelberg (2008)
7. Jegou, H, Douze, M, Schmid, C, Perez, P.: Aggregating local descriptors into a compact image representation. In: Proceedings of CVPR (2010)
8. Chatfield, K, Zisserman, A.: VISOR: towards on-the-fly large-scale object category retrieval. In: Lee, K.M, Matsushita, Y, Rehg, J.M, Hu, Z. (eds.) ACCV 2012, Part II. LNCS, vol. 7725, pp. 432–446. Springer, Heidelberg (2013)
9. Fernando, B, Tuytelaars, T.: Mining multiple queries for image retrieval: on-thefly learning of an object-specific mid-level representation. In: Proceedings of ICCV (2013)
10. Liu, Y, Xu, D, Tsang, I.W, Luo, J.: Using large-scale web data to facilitate textual query based retrieval of consumer photos. In: Proceedings of the 17th ACM International Conference on Multimedia, MM 2009, pp. 55–64 (2009)
11. Parkhi, O.M, Vedaldi, A, Zisserman, A.: On-the-fly specific person retrieval. In: International Workshop on Image Analysis for Multimedia Interactive Services. IEEE (2012)
12. Chatfield, K, Lempitsky, V, Vedaldi, A, Zisserman, A.: The devil is in the details: an evaluation of recent feature encoding methods. In: Proceedings of BMVC (2011)
13. Perronnin, F, Sanchez, J, Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Daniilidis, K, Maragos, P, Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010)
14. Perronnin, F, Akata, Z, Harchaoui, Z, Schmid, C.: Towards good practice in large-scale learning for image classification. In: Proceedings of CVPR, pp. 3482– 3489 (2012)
15. Jegou, H, Douze, M, Schmid, C.: Product quantization for nearest neighbor search. IEEE PAMI 33(1), 117–128 (2011)
16. Torralba, A, Fergus, R, Freeman, W.T.: 80 million tiny images: a large dataset for non-parametric object and scene recognition. IEEE PAMI 30, 1958–1970 (2008)
17. Raginsky, M, Lazebnik, S.: Locality sensitive binary codes from shift-invariant kernels. In: NIPS (2009)
18. LeCun, Y, Bottou, L, Bengio, Y, Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)
19. Krizhevsky, A, Sutskever, I, Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1106–1114 (2012)
20. Zeiler, M.D, Fergus, R.: Visualizing and understanding convolutional networks. CoRR abs/1311.2901 (2013)
21. Chatfield, K, Simonyan, K, Vedaldi, A, Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. In: Proceedings of BMVC (2014) 22. Everingham, M, Van Gool, L, Williams, C.K.I, Winn, J, Zisserman, A.: The PASCAL Visual Object Classes (VOC) challenge. IJCV 88, 303–338 (2010)
22. Huiskes, M.J, Lew, M.S.: The MIR flickr retrieval evaluation. In: MIR 2008: Proceedings of the 2008 ACM International Conference on Multimedia Information Retrieval (2008)
23. Huiskes, M.J, Thomee, B, Lew, M.S.: New trends and ideas in visual concept detection: the MIR flickr retrieval evaluation initiative. In: MIR 2010: Proceedings of the 2010 ACM International Conference on Multimedia Information Retrieval, pp. 527–536 (2010)
24. Donahue, J, Jia, Y, Vinyals, O, Hoffman, J, Zhang, N, Tzeng, E, Darrell, T.: DeCAF: a deep convolutional activation feature for generic visual recognition. CoRR abs/1310.1531 (2013)
25. Razavian, A, Azizpour, H, Sullivan, J, Carlsson, S.: CNN Features off-the-shelf: an Astounding Baseline for Recognition. CoRR abs/1403.6382 (2014)
26. Sanchez, J, Perronnin, F.: High-dimensional signature compression for large-scale image classification. In: Proceedings of CVPR (2011)
27. Jegou, H, Furon, T, Fuchs, J.J.: Anti-sparse coding for approximate nearest neighbor search. In: Proceedings of ICASSP, pp. 2029–2032 (2012)
28. Simonyan, K, Vedaldi, A, Zisserman, A.: Learning local feature descriptors using convex optimisation. IEEE PAMI 36, 1573–1585 (2014)
29. Parkhi, O.M, Simonyan, K, Vedaldi, A, Zisserman, A.: A compact and discriminative face track descriptor. In: Proceedings of CVPR. IEEE (2014)
30. Arandjelovic, R, Zisserman, A.: Three things everyone should know to improve object retrieval. In: Proceedings of CVPR (2012)
31. Sanchez, J, Perronnin, F, Emıdio de Campos, T.: Modeling the spatial layout of images beyond spatial pyramids. Pattern Recogn. Lett. 33, 2216–2223 (2012)
32. Arandjelovic, R, Zisserman, A.: All about VLAD. In: Proceedings of CVPR (2013)
33. Jia, Y.: Caffe: an open source convolutional architecture for fast feature embedding (2013). http://caffe.berkeleyvision.org/
34. Shalev-Shwartz, S, Singer, Y, Srebro, N.: Pegasos: primal estimated sub-gradient SOlver for SVM. In: Proceedings of ICML, vol. 227 (2007)