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1 BAG-OF-WORDS MODEL The slides are from several sources through James Hays (Brown); Silvio Savarese (U. of Michigan); Bill Freeman and Antonio Torralba (MIT), including their own slides.

2 Visual Perceptual Object and Recognition Sensory Augmented Tutorial Computing Kristen Grauman Limitations window-based detection. Not all objects are box shaped.

3 Kristen Grauman Visual Perceptual Object and Recognition Sensory Augmented Tutorial Computing Non-rigid, deformable objects not captured well with representations assuming a fixed 2 structure; or must assume fixed viewpoint Objects with less-regular textures not captured well with holistic appearance-based descriptions

4 If considering windows in isolation, context is lost Visual Perceptual Object and Recognition Sensory Augmented Tutorial Computing Sliding window Detector s view Often entails large cropped training sets, which is expensive. Figure credit: Derek Hoiem Kristen Grauman

5 Object Bag of words

6 Definition of the Bag of 'Words' Independent features face bike violin which are parts of an object. See examples above.

7 Definition of the Bag of 'Words' The independent features are shown in a histogram representation. codewords dictionary

8 Representation Recognition feature detection & representation codewords dictionary image representation learning category models (and/or) classifiers category decision

9 Bag-of-words (features) learning steps 1. Extract features 2. Learn visual vocabulary 3. Quantize features using visual vocabulary 4. Represent images by frequencies of visual words

10 1. Feature extraction Compute descriptor Normalize patch Detect patches Slide credit: Josef Sivic

11 2. Learning the visual vocabulary Slide credit: Josef Sivic

12 2. Learning the visual vocabulary Clustering Slide credit: Josef Sivic

13 2. Learning the visual vocabulary Visual vocabulary Clustering Slide credit: Josef Sivic

14 Example codebook K-means clustering the value K is given = visual vocabulary Appearance codebook Source: B. Leibe

15 Another codebook Appearance codebook Source: B. Leibe

16 3. Bag of word representation frequency. codewords Codewords dictionary

17 ...gives the category models Class 1 Class N

18 Representation 1. feature detection & representation 2. codewords dictionary 3. image representation category models

19 Learning and Recognition codewords dictionary category models (and/or) classifiers category decision

20 Visual vocabularies: Issues How to choose vocabulary size? Too small: visual words not representative of all patches. Too large: quantization artifacts, overfitting. Computational efficiency use: Vocabulary trees Nister & Stewenius, 2006

21 Vocabulary Trees D. Nistér and H. Stewénius. Scalable recognition with a vocabulary tree. CVPR, , SIFT descriptors. Moving into the feature space...

22 ...moving into the feature space etc.

23 Points in a region are the closest to the cluster center. Hierarchial clustering. The training data is divided in the closest k clusters. Each cell is then split into k new parts are the process is applied recursively.

24 Hierarchial clustering. k = 3 and two layers here. black 1 green 3 blue 9

25 The feature space. Smaller "dots" are deeper.

26 In reality! Six levels of hierarchy. k = 10 nodes at every branch. Each point 128 vector (SIFT). 1M leaf nodes needs 143MB of memory.

27 The image database was created in a hierarchical manner. Recovery (testing) is also hierarchical. A descriptor (SIFT) is propagated down the tree. At each layer the closest cluster is selected from the k candidated. The tree defines the vocabulary tree hierarchically. A descriptor gets a score in every layer relative to the closest training descriptor. The final scoring is the sum of all the layers of the vocabulary tree.

28 A few SIFT-s in four images. Two descriptors, give other images, not the right ones... The whole image is shown for the illustration not the descriptor.

29 Scoring three instances.

30 If we can get repeatable, discriminative features, then recognition can scale to very large databases using the vocabulary tree and indexing approach. Database creation 2-3 days. Test about 1 second per image. Searching for face images is much less reliable...

31 Performance red - target with ground truth blue - independent from database 1 million images <==(from 7 movies) plus 6376 images with ground truth==>

32 6376 ground truth image Size Matters Improves Retrieval Improves Speed Leaf nodes with branch factor 8...

33 But what about layout? All of these images have the same color histogram.

34 Spatial pyramid Compute histogram in each spatial bin.

35 Spatial pyramid representation Extension of a bag of features. Locally orderless representation at several levels of resolution. level 0 Lazebnik, Schmid & Ponce (CVPR 2006)

36 Spatial pyramid representation Extension of a bag of features Locally orderless representation at several levels of resolution level 0 level 1 Lazebnik, Schmid & Ponce (CVPR 2006)

37 Spatial pyramid representation Extension of a bag of features Locally orderless representation at several levels of resolution level 0 level 1 level 2 Lazebnik, Schmid & Ponce (CVPR 2006)

38 Feature extraction with SIFT in dense regular grid (8 pixel aside). Images are 300x250 pixels. Vocabulary sizes 200. Each of the 15 categories has 200 to 400 images. Training: 100 images per class. Testing: all the remaning images. Number of pyramid levels = L. Also independently in a given "l" level.

39 Scene category dataset Multi-class classification results (100 training images per class)

40 Weakness of Bag of Words models No rigorous geometric information of the object components. All have equal probability. Not extensively tested yet for view point invariance scale invariance. Segmentation and localization is unclear. Location information is also important.

41 Invariance issues Scale rotation view point - occlusions Implicitly taken only. The right detectors and descriptors may improve. Kadir and Brady. 2003

42 Slide credit: Ondrej Chum Spatial Verification... Real objects have consistent geometry. Query Query image with high BoW similarity other image with high BoW similarity Both image pairs have many visual words in common.

43 Slide credit: Ondrej Chum...can work many times. Query Query after robust matching high BoW similarity after robust matching low BoW similarity Only some of the matches are mutually consistent.

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