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# An Exemplar Model For Learning Object Classes

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### An Exemplar Model For Learning Object Classes

1. 1. An Exemplar Model for Learning Object Classes<br />Authors: Ondrej Chum Andrew Zisserman@University of Oxford<br />Presenter: Shao-Chuan Wang<br />
2. 2. An Exemplar Model for Learning Object Classes<br />Objective:<br />Give training images known to contain instances of an object class, without specifying locations and scales.<br />Detect and localize object<br />Kea Ideas: <br />Learn region of interest (ROI) around class instance in weakly supervised training data.<br />Based on discriminative features to initialize ROI for the optimization problem<br />
3. 3. An Exemplar Model for Learning Object Classes<br />Exemplar model:<br />Detection (cost function):<br />X<br />Y<br />X: exemplar set<br />X^w: PHOW descriptor<br />X^e: PHOG descriptor<br />A: aspect ratio of target region<br />d: distance function<br />/mu: mean of exemplars’ aspect ratio<br />/sigma: std of exemplars’ aspect ratio<br />/alpha, /beta: weighting to be tuned/learned<br />
4. 4. An Exemplar Model for Learning Object Classes<br />Learning the exemplar model:<br />Learn the regions in all images simultaneously.<br />How to Determine initial ROI?<br />&gt; By discriminative features<br />
5. 5. Top 10 most discriminative visual words<br />Discriminative features<br />Definition:<br />
6. 6. Constructing ROI exemplars: Algorithm<br />
7. 7. Constructing ROI exemplars: Algorithm<br />Initialization<br />Calculate discriminability of visual words<br />Initialize the ROI in each training image by a bounding box of the 64 most discriminative features<br />Optimization of cost function<br />Find the ROI to minimize the cost function with eta = 0<br />Re-initialization by detection<br />Refinement<br />Enlarge the ROI in the training images by 10%<br />Calculate discriminability of visual words using only the features inside the ROI<br />Optimization of cost function (goto 2.)<br />
8. 8. Constructing ROI exemplars: Algorithm<br />Initialization<br />Calculate discriminability of visual words<br />Initialize the ROI in each training image by a bounding box of the 64 most discriminative features<br />Optimization of cost function<br />Find the ROI to minimize the cost function with eta = 0<br />Re-initialization by detection<br />Refinement<br />Enlarge the ROI in the training images by 10%<br />Calculate discriminability of visual words using only the features inside the ROI<br />Optimization of cost function (goto 2.)<br />
9. 9. Constructing ROI exemplars: Algorithm<br />Initialization<br />Calculate discriminability of visual words<br />Initialize the ROI in each training image by a bounding box of the 64 most discriminative features<br />Optimization of cost function<br />Find the ROI to minimize the cost function with eta = 0<br />Re-initialization by detection<br />Refinement<br />Enlarge the ROI in the training images by 10%<br />Calculate discriminability of visual words using only the features inside the ROI<br />Optimization of cost function (goto 2.)<br />
10. 10. Constructing ROI exemplars: Algorithm<br />Initialization<br />Calculate discriminability of visual words<br />Initialize the ROI in each training image by a bounding box of the 64 most discriminative features<br />Optimization of cost function<br />Find the ROI to minimize the cost function with eta = 0<br />Re-initialization by detection.<br />Refinement<br />Enlarge the ROI in the training images by 10%<br />Calculate discriminability of visual words using only the features inside the ROI<br />Optimization of cost function (goto 2.)<br />
11. 11. Constructing ROI exemplars: Algorithm<br />Initialization<br />Calculate discriminability of visual words<br />Initialize the ROI in each training image by a bounding box of the 64 most discriminative features<br />Optimization of cost function<br />Find the ROI to minimize the cost function with eta = 0<br />Re-initialization by detection. <br />Refinement<br />Enlarge the ROI in the training images by 10%<br />Calculate discriminability of visual words using only the features inside the ROI<br />Optimization of cost function (goto 2.)<br />
12. 12. Constructing ROI exemplars: Algorithm<br />Initialization<br />Calculate discriminability of visual words<br />Initialize the ROI in each training image by a bounding box of the 64 most discriminative features<br />Optimization of cost function<br />Find the ROI to minimize the cost function with eta = 0<br />Re-initialization by detection.<br />Refinement<br />Enlarge the ROI in the training images by 10%<br />Calculate discriminability of visual words using only the features inside the ROI<br />Optimization of cost function (goto 2.)<br />
13. 13. Constructing ROI exemplars: Algorithm<br />Three stages of the optimization process<br />Initialization<br />Optimization<br />Re-initialization<br />via<br />detection<br />
14. 14. Using the exemplar model<br />Object Detection <br />Hypothesis<br />Score of a hypothesis<br />n_(w,R): the number of exemplar Images consistent with the hypothesis<br />#w: the number of appearances of the visual word w in the exemplar images<br />Clustering<br />20 strongest hypotheses are tested on each test image<br />
15. 15. Using other models<br />Training:<br />Train an SVM, using features within ROI by exemplar models<br />Object detection<br />Scores are ranked by SVM score<br />
16. 16. Results<br />
17. 17. Conclusion<br />When constructing exemplars’ ROI, they use discriminability to initialize bounding box<br />In detection, they used relative position of bounding boxes and visual words to try the most probable hypotheses.<br />It may failed to detect when significant class variability in the exemplars, such as people class.<br />