SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang,...
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Transcript of SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang,...
![Page 1: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/1.jpg)
SVM-KNN Discriminative Nearest Neighbor Classification for Visual
Category RecognitionHao Zhang, Alex Berg, Michael
Maire, Jitendra Malik
![Page 2: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/2.jpg)
Multi-class Image ClassificationCaltech 101
![Page 3: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/3.jpg)
Vanilla Approach
1. For each image, select interest points2. Extract features from patches around all
interest points3. Compute the distance between images
1. Hack a distance metric for the features
4. Use the pair-wise distances between the test and database images in a learning algorithm
1. KNN-SVM
![Page 4: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/4.jpg)
KNN-SVM
• For each test image– Select the K nearest neighbors– If all K neighbors are one class, done– Else, train an SVM using only those K points
• DAGSVM
• Too slow to compute K nearest neighbors– Use a simpler distance metric to select N
neighbors
![Page 5: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/5.jpg)
Features - Texture
• Compute texons by using some filter bank
• X² distance between texons
• Marginal distance– Sum of responses for all histograms, then
computed X²
![Page 6: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/6.jpg)
Features - Tangent Distance
• Each image along with its transformations forms a linear subspace
![Page 7: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/7.jpg)
Comparison
![Page 8: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/8.jpg)
Features - Shape Context
![Page 9: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/9.jpg)
Features – Geometric Blur
![Page 10: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/10.jpg)
Geometric Blur
![Page 11: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/11.jpg)
Geometric Blur
![Page 12: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/12.jpg)
KNN-SVN Results
How is K chosen?
![Page 13: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/13.jpg)
Learning Distance MetricsFrome, Singer, Malik
• Classification just by distances is too rough• Learn a distance metric for every examplar image
– Each image is divided into patches– Set of features has its own distance metric– Learn a weighing of the different patches
![Page 14: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/14.jpg)
Training
• Use triplets of images (Focal,Idissimilar,Isimilar)
– Dissimilar and similar have to follow
![Page 15: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/15.jpg)
Beyond Bags of Features: Spatial Pyramid Matching for
Recognizing Natural Scene Categories
S. Lazebnik, C. Schmid, J. Ponce
![Page 16: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/16.jpg)
Bags of Features with Pyramids
![Page 17: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/17.jpg)
Intersection of Histograms
• Compute features on a random set of images
• Use kmeans to extract 200-400 clusters
![Page 18: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/18.jpg)
Features
• Weak Features– Oriented edge points, Gist
• Strong Features– SIFT
![Page 19: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/19.jpg)
Results on scenes
![Page 20: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/20.jpg)
Results on Caltech 101 and Graz
![Page 21: SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.](https://reader030.fdocuments.in/reader030/viewer/2022032709/56649ed85503460f94be6d13/html5/thumbnails/21.jpg)
Lessons Learned
• Use dense regular grid instead of interest points
• Latent Dirichlet Analysis negatively affects classification– Unsupervised dimensionality reduction– Explain scene with topics
• Pyramids only improve by 1-2%– Robust against wrong pyramid level