This document summarizes an exemplar model for learning object classes from weakly supervised training data. The model learns a region of interest (ROI) around each object instance by initializing the ROI based on the locations of the most discriminative visual words, then refining the ROI through iterative optimization. To detect objects, the model scores hypotheses based on the number of exemplar images consistent with each hypothesis and uses the highest scoring hypotheses to detect objects.

1. An Exemplar Model for Learning Object ClassesAuthors: Ondrej Chum Andrew Zisserman@University of OxfordPresenter: Shao-Chuan Wang

2. An Exemplar Model for Learning Object ClassesObjective:Give training images known to contain instances of an object class, without specifying locations and scales.Detect and localize objectKea Ideas: Learn region of interest (ROI) around class instance in weakly supervised training data.Based on discriminative features to initialize ROI for the optimization problem

3. An Exemplar Model for Learning Object ClassesExemplar model:Detection (cost function):XYX: exemplar setX^w: PHOW descriptorX^e: PHOG descriptorA: aspect ratio of target regiond: distance function/mu: mean of exemplars’ aspect ratio/sigma: std of exemplars’ aspect ratio/alpha, /beta: weighting to be tuned/learned

4. An Exemplar Model for Learning Object ClassesLearning the exemplar model:Learn the regions in all images simultaneously.How to Determine initial ROI?> By discriminative features

5. Top 10 most discriminative visual wordsDiscriminative featuresDefinition:

6. Constructing ROI exemplars: Algorithm

7. Constructing ROI exemplars: AlgorithmInitializationCalculate discriminability of visual wordsInitialize the ROI in each training image by a bounding box of the 64 most discriminative featuresOptimization of cost functionFind the ROI to minimize the cost function with \beta = 0Re-initialization by detectionRefinementEnlarge the ROI in the training images by 10%Calculate discriminability of visual words using only the features inside the ROIOptimization of cost function (goto 2.)

8. Constructing ROI exemplars: AlgorithmInitializationCalculate discriminability of visual wordsInitialize the ROI in each training image by a bounding box of the 64 most discriminative featuresOptimization of cost functionFind the ROI to minimize the cost function with \beta = 0Re-initialization by detectionRefinementEnlarge the ROI in the training images by 10%Calculate discriminability of visual words using only the features inside the ROIOptimization of cost function (goto 2.)

9. Constructing ROI exemplars: AlgorithmInitializationCalculate discriminability of visual wordsInitialize the ROI in each training image by a bounding box of the 64 most discriminative featuresOptimization of cost functionFind the ROI to minimize the cost function with \beta = 0Re-initialization by detectionRefinementEnlarge the ROI in the training images by 10%Calculate discriminability of visual words using only the features inside the ROIOptimization of cost function (goto 2.)

10. Constructing ROI exemplars: AlgorithmInitializationCalculate discriminability of visual wordsInitialize the ROI in each training image by a bounding box of the 64 most discriminative featuresOptimization of cost functionFind the ROI to minimize the cost function with \beta = 0Re-initialization by detection.RefinementEnlarge the ROI in the training images by 10%Calculate discriminability of visual words using only the features inside the ROIOptimization of cost function (goto 2.)

11. Constructing ROI exemplars: AlgorithmInitializationCalculate discriminability of visual wordsInitialize the ROI in each training image by a bounding box of the 64 most discriminative featuresOptimization of cost functionFind the ROI to minimize the cost function with \beta = 0Re-initialization by detection. RefinementEnlarge the ROI in the training images by 10%Calculate discriminability of visual words using only the features inside the ROIOptimization of cost function (goto 2.)

12. Constructing ROI exemplars: AlgorithmInitializationCalculate discriminability of visual wordsInitialize the ROI in each training image by a bounding box of the 64 most discriminative featuresOptimization of cost functionFind the ROI to minimize the cost function with \beta = 0Re-initialization by detection.RefinementEnlarge the ROI in the training images by 10%Calculate discriminability of visual words using only the features inside the ROIOptimization of cost function (goto 2.)

13. Constructing ROI exemplars: AlgorithmThree stages of the optimization processInitializationOptimizationRe-initializationviadetection

14. Using the exemplar modelObject Detection HypothesisScore of a hypothesisn_(w,R): the number of exemplar Images consistent with the hypothesis#w: the number of appearances of the visual word w in the exemplar imagesClustering20 strongest hypotheses are tested on each test image

15. Using other modelsTraining:Train an SVM, using features within ROI by exemplar modelsObject detectionScores are ranked by SVM score

16. Results

17. ConclusionWhen constructing exemplars’ ROI, they use discriminability to initialize bounding boxIn detection, they used relative position of bounding boxes and visual words to try the most probable hypotheses.It may failed to detect when significant class variability in the exemplars, such as people class.