Convolutional Features for Instance Search
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This document discusses techniques for instance search using convolutional neural network features. It presents two papers by the author on this topic. The first paper uses bags-of-visual-words to encode convolutional features for scalable instance search. The second paper explores using region-level features from Faster R-CNN models for instance search and compares different fine-tuning strategies. The document outlines the methodology, experiments on standard datasets, and conclusions from both papers.
![16
Bag of Words Framework
(336x256)
Resolution
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VGG16[1]
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[1]Simonyan K., Zisserman A., Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv 2014
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Convolutional Features for Instance Search
- 1. Convolutional Features for Instance Search Amaia Salvador 03/05/2016
- 2. 2 Related Publications E. Mohedano, A. Salvador, K. McGuinness, F. Marques, N. E. O'Connor and X. Giro, Bags of Local Convolutional Features for Scalable Instance Search Accepted at ICMR 2016 A. Salvador, X. Giro, F. Marques, S. Satoh, Faster R-CNN Features for Instance Search Accepted at DeepVision CVPRW 2016
- 3. Part I E. Mohedano, A. Salvador, K. McGuinness, F. Marques, N. E. O'Connor and X. Giro, Bags of Local Convolutional Features for Scalable Instance Search
- 4. Visual Image Retrieval 4Image Database Visual Query “A dog” Expected outcome:
- 5. Visual Instance Retrieval 5Image Database Visual Query “This dog” Expected outcome:
- 6. Visual Instance Retrieval 6 Image RepresentationsQuery image Image Database Image Matching Ranking List Similarity score Image ... 0.98 0.97 0.10 0.01 v = (v1 , …, vn ) v1 = (v11 , …, v1n ) vk = (vk1 , …, vkn ) ... Similarity Metric (e.g. cosine similarity) ...
- 7. 7 v1 = (v11 , …, v1n ) vk = (vk1 , …, vkn ) ... INVERTED FILE word Image ID 1 1, 12, 2 1, 30, 102 3 10, 12 4 2,3 6 10 ... Local hand-crafted features (e.g. SIFT) Bag of Visual WordsN-Dimensional feature space Image Representations High-dimensional Highly sparse
- 8. 8 Image Representations Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105). Convolutional Neural Networks
- 9. 9 Image Representations Babenko, A., Slesarev, A., Chigorin, A., & Lempitsky, V. (2014). Neural codes for image retrieval. In ECCV 2014 Razavian, A., Azizpour, H., Sullivan, J., & Carlsson, S. (2014). CNN features off-the-shelf: an astounding baseline for recognition. In DeepVision CVPRW 2014 Convolutional Neural Networks FC layers as global feature representation
- 10. 10 Image Representations Babenko, A., & Lempitsky, V. (2015). Aggregating local deep features for image retrieval. ICCV 2015 Tolias, G., Sicre, R., & Jégou, H. (2015). Particular object retrieval with integral max-pooling of CNN activations. ICLR 2015 Kalantidis, Y., Mellina, C., & Osindero, S. (2015). Cross-dimensional Weighting for Aggregated Deep Convolutional Features. arXiv preprint arXiv:1512.04065. Convolutional Neural Networks sum/max pooled conv features as global representation
- 11. 11 Image Representations Ng, J., Yang, F., & Davis, L. (2015). Exploiting local features from deep networks for image retrieval. In DeepVision CVPRW 2015 Convolutional Neural Networks conv features encoded with VLAD as global representation
- 12. 12 Motivation Dataset Complexity TRECVID Instance Search 464 hours of video content
- 13. 13 Motivation: Image Representations High-dimensional & Sparse Bag of Visual Words Compact & Dense (e.g. sum/max pooling conv feats, FC feats) Capacity? High-dimensional & Dense (e.g. VLAD encoding) Scalability?
- 14. Methodology
- 15. 15 Bag of Words Framework
- 16. 16 Bag of Words Framework (336x256) Resolution conv5_1 from VGG16[1] (42x32) [1]Simonyan K., Zisserman A., Very Deep Convolutional Networks for Large-Scale Image Recognition, arXiv 2014 25K centroids 25K-D vector
- 17. 17 Instance Retrieval Query Representation ... ... ... ... ... ... Global Search (GS) Local Search (LS)
- 18. 18 Spatial Reranking Image RepresentationsQuery image Image Database Image Matching Ranking List v = (v1 , …, vn ) v1 = (v11 , …, v1n ) vk = (vk1 , …, vkn ) ... Similarity Metric (cosine similarity) ... Top M images are locally analyzed and reranked (M = 100)
- 19. 19 Spatial Reranking All window combinations with: Query Image Target image in top M ranking ... ...
- 20. 20 Query Expansion Image RepresentationsQuery image Image Database Image Matching Ranking List v = (v1 , …, vn ) v1 = (v11 , …, v1n ) vk = (vk1 , …, vkn ) ... Similarity Metric (cosine similarity) ... Top N images are added to the query for a new search (N = 5)
- 21. Experiments
- 22. 22 Datasets Paris Buildings 6k Oxford Buildings 5k TRECVID Instance Search 2013 (subset of 23k frames) Philbin, J. , Chum, O. , Isard, M. , Sivic, J. and Zisserman, A. Object retrieval with large vocabularies and fast spatial matching, CVPR 2007 Philbin, J. , Chum, O. , Isard, M. , Sivic, J. and Zisserman, A. Lost in Quantization: Improving Particular Object Retrieval in Large Scale Image Databases. CVPR 2008 Smeaton, A. F., Over, P., & Kraaij, W. Evaluation campaigns and TRECVid. ACM MM Multimedia information retrieval Workshop 2006
- 23. 23 Results I: SoA Comparison
- 24. 24 Results II: TRECVid INS
- 26. 26 Conclusion BoW encoding of convolutional features • High-dimensional sparse representation suitable for fast retrieval • Competitive results in two image retrieval benchmarks • Well suited and more robust for scenarios where only small number of features are in the target images are relevant to the query (INS).
- 27. Part II A. Salvador, X. Giro, F. Marques, S. Satoh, Faster R-CNN Features for Instance Search
- 28. 28 Reminder: Spatial Reranking Query Image Target image in top M ranking ... ...
- 29. 29 Reminder: Spatial Reranking Koen E. A. van de Sande, Jasper R. R. Uijlings, Theo Gevers, Arnold W. M. Smeulders. Segmentation as Selective Search for Object Recognition, ICCV 2011 Object Proposals
- 30. 30 Image & Region Representations “dog” CNN Architectures plant, table, dog CNN CNN Image Classification Object Detection
- 31. 31 Image & Region Representations Faster R-CNN Conv layers Region Proposal Network FC6 Class probabilities FC7 FC8 RPN Proposals RoI Pooling Conv5_3 RPN Proposals Ren, S., He, K., Girshick, R., & Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. NIPS 2015
- 32. 32 Image & Region Representations Faster R-CNN Conv layers Region Proposal Network FC6 Class probabilities FC7 FC8 RPN Proposals RoI Pooling Conv5_3 RPN Proposals Ren, S., He, K., Girshick, R., & Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. NIPS 2015 Image representation Region Representation
- 33. 33 Image & Region Representations Image representation Region Representation (for reranking) RoI Pooling Conv5_3 RoI Pooling sum-pooling max-pooling DD
- 34. 34 Fine tuning for query objects Faster R-CNN Conv layers Region Proposal Network FC6 Class probabilities FC7 FC8 RPN Proposals RoI Pooling Conv5_3 RPN Proposals Train object detector for query instances using query images as training data
- 35. 35 Fine tuning for query objects FT #1: Train FC layers only Conv layers Region Proposal Network FC6 Class probabilities FC7 FC8 RPN Proposals RoI Pooling Conv5_3 RPN Proposals
- 36. 36 Fine tuning for query objects FT #2: Train all weights after conv2 Conv layers Region Proposal Network FC6 Class probabilities FC7 FC8 RPN Proposals RoI Pooling Conv5_3 RPN Proposals
- 37. 37 Spatial Reranking Strategies Class-agnostic Spatial Reranking (CA-SR) Query Image Database Image FC6 Class probabilities FC7 FC8 ... Class-specific Spatial Reranking (CS-SR)
- 38. 38 Results
- 39. 39Query image Top N retrieved images
- 40. 40 Conclusion Faster R-CNN for Instance Search • Suitable to obtain image and region features in a single forward pass • Fine tuning as an effective solution to boost retrieval performance (subject to application time constraints) Conv layers Region Proposal Network FC6 Class probabilities FC7 FC8 RPN Proposals RoI Pooling Conv5_3 RPN Proposals Image representation Region Representation
- 41. 41 Thank you for your attention ! E. Mohedano, A. Salvador, K. McGuinness, F. Marques, N. E. O'Connor and X. Giro, Bags of Local Convolutional Features for Scalable Instance Search Accepted at ICMR 2016 A. Salvador, X. Giro, F. Marques, S. Satoh, Faster R-CNN Features for Instance Search Accepted at DeepVision CVPRW 2016