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Analyzing object categories via novel category ranking measures defined on visual feature embeddings

Sarvadevabhatla, Ravi Kiran and Meesala, Raviteja and Hegde, Manjunath and Babu, Venkatesh R (2016) Analyzing object categories via novel category ranking measures defined on visual feature embeddings. In: 10th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), DEC 18-22, 2016, Indian Inst Technol, Guwahati, INDIA.

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Official URL: http://dx.doi.org/10.1145/3009977.3010037


Visualizing 2-D/3-D embeddings of image features can help gain an intuitive understanding of the image category landscape. However, popular visualization methods of visualizing such embeddings (e.g. color-coding by category) are impractical when the number of categories is large. To address this and other shortcomings, we propose novel quantitative measures defined on image feature embeddings. Each measure produces a ranked ordering of the categories and provides an intuitive vantage point from which to view the entire set of categories. As an experimental testbed, we use deep features obtained from category-epitomes, a recently introduced minimalist visual representation, across 160 object categories. We embed the features in a visualization friendly yet similarity-preserving 2-D manifold and analyze the inter/intra-category distributions of these embeddings using the proposed measures. Our analysis demonstrates that the category ordering methods enable new insights for the domain of large-category object representations. Moreover, our ordering measure approach is general in nature and can be applied to any feature-based representation of categories.

Item Type: Conference Proceedings
Additional Information: Copy right for this article belongs to the ASSOC COMPUTING MACHINERY, 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
Department/Centre: Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited: 15 Jul 2017 07:40
Last Modified: 15 Jul 2017 07:40
URI: http://eprints.iisc.ac.in/id/eprint/57430

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