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A.: Blocks that shout: Distinctive parts for scene classification (2013)
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BibTeX
@MISC{Juneja13a.:blocks,
author = {Mayank Juneja and Andrea Vedaldi and C. V. Jawahar and Andrew Zisserman},
title = {A.: Blocks that shout: Distinctive parts for scene classification},
year = {2013}
}
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Abstract
The 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. 1.
Keyphrases
distinctive part scene classification fisher vector model entropy-rank curve mit scene new representation single part occurrence useful part additional part instance automatic discovery exemplar svm previous state-of-the-art classification performance previ-ous part-learning method part appearance effective method scene class scene cat-egorisation
