Convolutional Patch Representations for Image Retrieval An unsupervised approach
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1. The document presents an unsupervised approach using convolutional neural networks to generate patch-level descriptors for image retrieval. 2. It trains a convolutional kernel network on unlabeled image patches to learn feature representations in a kernel space without requiring manual labels. 3. Experiments show the convolutional kernel descriptors achieve similar or better performance than supervised convolutional neural networks on standard patch and image retrieval datasets while requiring less training time.
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![Related Work
• Deep patch descriptors
3 different kind of supervision:
1. Category labels of ImageNet. [Long et al, 2014]
2. Surrogate patch labels: Each class is a given patch under different transformations [Fischer et al, 2014]
3. Matching/non-matching pairs. [Simo-Serra et al, 2015]
Works focussed in patch-level metrics, not image retrieval.
All approaches requiered some kind of supervision.](https://image.slidesharecdn.com/patch-cknv429-160329124825/85/Convolutional-Patch-Representations-for-Image-Retrieval-An-unsupervised-approach-9-320.jpg)
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Convolutional Patch Representations for Image Retrieval An unsupervised approach
- 1. Convolutional Patch Representations for Image Retrieval: an Unsupervised Approach 29th Mar 2016 Original slides by Eva Mohedano Insight Centre for Data Analytics (Dublin City University Mattis Paulin, Julien Mairal, Matthijs Douze, Zaid Harchaoui, Florent Perronnin, Cordelia Schmidt
- 2. Overview Published ICCV 2015 (A.K.A. Local Convolutional Features With Unsupervised Training for Image Retrieval) Deep Convolutional Architecture to produce patch-level descriptors • Unsupervised framework • Comparison in patch and retrieval datasets • “RomePatches” dataset
- 3. Related Work • Shallow patch descriptors • Deep learning for image retrieval • Deep patch descriptors
- 4. Related Work • Shallow patch descriptors SIFT – Scale-Invariant Feature Transform - stereo matching - retrieval - classification SURF, BRIEF, LIOP, (…) Hand crafted → Relatively small number of parameters. Note: A patch is an image region extracted from an image.
- 5. Related Work • Deep learning for image retrieval CNN learned on a sufficiently large labeled dataset (ImageNet) generates intermediate layers that can be used as image descriptors. Those descriptors work for a wide variety of tasks, including image retrieval
- 6. Related Work • Deep learning for image retrieval source image: http://pubs.sciepub.com/ajme/2/7/9/
- 7. Related Work • Deep learning for image retrieval source image: http://pubs.sciepub.com/ajme/2/7/9/ Fully connected layers → Global Image Descriptors ● Compact representation ● lack of geometric invariance Below state-of-the art in image retrieval Compute at different scales (Babenko, Razavian)
- 8. Related Work • Deep learning for image retrieval source image: http://pubs.sciepub.com/ajme/2/7/9/ Convolutional layers
- 9. Related Work • Deep patch descriptors 3 different kind of supervision: 1. Category labels of ImageNet. [Long et al, 2014] 2. Surrogate patch labels: Each class is a given patch under different transformations [Fischer et al, 2014] 3. Matching/non-matching pairs. [Simo-Serra et al, 2015] Works focussed in patch-level metrics, not image retrieval. All approaches requiered some kind of supervision.
- 10. Image Retrieval Pipeline • Interest point detection Hessian-Affine detector. Rotation invariance. • Interest point description Feature representation in a Euclidean space • Patch Matching VLAD encoding. Power normalization with exponent 0.5 + L2-norm.
- 11. Image Retrieval Pipeline • Interest point detection Hessian-Affine detector. Rotation invariance. • Interest point description Feature representation in a Euclidean space • Patch Matching VLAD encoding. Power normalization with exponent 0.5 + L2-norm.
- 12. Convolutional Descriptors Patch size = 51x51 – Optimal for SIFT on Oxford dataset. CNN extended to retrieval by: • Encoding local descriptors with model trained with an unrelated classification task • Devising a surrogate classification problem that is as related as possible to image retrieval: • Using unsupervised learning: Convolutional Kernel Network
- 13. Convolutional Descriptors • Using unsupervised learning: Convolutional Kernel Network Feature representation based in a kernel (feature) map -- Data independent
- 14. Convolutional Descriptors • Using unsupervised learning: Convolutional Kernel Network Projection in Hilbert space Explicit kernel map can be computed to approximate it for computational efficiency. - Sub-sample of patches - Stochastic Gradient Optimization
- 15. Convolutional Descriptors • Using unsupervised learning: Convolutional Kernel Network 4 possible inputs From left to right: CKN-raw, CKN-mean subs, CKN-white (mean subs + PCA-whitening), CKN-grad (fully invariant to color) Only CKN-raw, CKN-white and CKN-grad are evaluated.
- 16. Experiments Datasets: 1. Rome Patches-Image 2. Oxford 3. UKbench and Holidays CKN trained on 1M sub-patches. 300K iterations. Mini-batches size of 1000.
- 17. Experiments
- 18. Conclusions • CKN offer similar and sometimes better performance than CNN in the context of patch description. • Good patch retrieval translates into good image retrieval. • CKNs are orders of magnitude faster to train than CNNs (10 min vs 2-3 days on a modern GPU) • Fully unsupervised – no labels.
- 19. Resources RomePatches+Code (Although code is not accessible!) Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks - Code with augmentations in matlab - Code for training models. - Models already trained :-) Triplet’s net + Code !! - Greyscale local patches of 32x32. Tested in matching datasets