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Blocks that Shout: Distinctive Parts for Scene ClassificationAuthors: Mayank Juneja,Andrea Vedaldi, C V Jawahar,Andrew Zisserman Conference: International Conference on Computer Vision and Pattern Recognition, 23-28 June. 2013, Oregon, USA. Date: 2013-06-23 Report no: IIIT/TR/2013/61 AbstractThe automatic discovery of distinctive parts for an ob- ject or scene class is challenging since it requires simulta- neously to learn the part appearance and also to identify the part occurrences in images. In this paper, we propose a simple, efficient, and effective method to do so. We ad- dress this problem by learning parts incrementally, starting from a single part occurrence with an Exemplar SVM. In this manner, additional part instances are discovered and aligned reliably before being considered as training exam- ples. We also propose entropy-rank curves as a means of evaluating the distinctiveness of parts shareable between categories and use them to select useful parts out of a set of candidates. We apply the new representation to the task of scene cat- egorisation on the MIT Scene 67 benchmark. We show that our method can learn parts which are significantly more in- formative and for a fraction of the cost, compared to previ- ous part-learning methods such as Singh et al . [28]. We also show that a well constructed bag of words or Fisher vector model can substantially outperform the previous state-of- the-art classification performance on this data. Full paper: pdf Centre for Visual Information Technology |
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