Object Categorization by an Augmented Bag-of-Visual-Words Approach
In object categorization, the bag-of-visual-words approach has demonstrated promising performance. However, one problem with it is that spatial information of object parts is discarded completely. This paper proposes to incorporate spatial information into bag of visual words framework. First, a set of flexible category specific key point patterns are selected from training images. Then, we use them to filter key points in an image, and estimate the object position using coordinates of filtered key points. After that, a set of windows are set into the image, based on the estimated object position. Then, a histogram is created for each window, which is concatenated at last as the final image representation. SVM is used for classification. We conducted experiments on the dataset of Caltech 101, and measurable improvement was achieved by the proposed method.
KeywordsTarget Object Visual Word Training Image Regular Grid Image Representation
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