Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99321
Title: Object recognition by discriminative combinations of line segments, ellipses, and appearance features
Authors: Chia, Alex Yong Sang
Rahardja, Susanto
Rajan, Deepu
Leung, Maylor Karhang
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Chia, A. Y.-S., Rajan, D., Leung, M. K., & Rahardja, S. (2012). Object Recognition by Discriminative Combinations of Line Segments, Ellipses, and Appearance Features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(9), 1758-1772.
Series/Report no.: IEEE transactions on pattern analysis and machine intelligence
Abstract: We present a novel contour-based approach that recognizes object classes in real-world scenes using simple and generic shape primitives of line segments and ellipses. Compared to commonly used contour fragment features, these primitives support more efficient representation since their storage requirements are independent of object size. Additionally, these primitives are readily described by their geometrical properties and hence afford very efficient feature comparison. We pair these primitives as shape-tokens and learn discriminative combinations of shape-tokens. Here, we allow each combination to have a variable number of shape-tokens. This, coupled with the generic nature of primitives, enables a variety of class-specific shape structures to be learned. Building on the contour-based method, we propose a new hybrid recognition method that combines shape and appearance features. Each discriminative combination can vary in the number and the types of features, where these two degrees of variability empower the hybrid method with even more flexibility and discriminative potential. We evaluate our methods across a large number of challenging classes, and obtain very competitive results against other methods. These results show the proposed shape primitives are indeed sufficiently powerful to recognize object classes in complex real-world scenes.
URI: https://hdl.handle.net/10356/99321
http://hdl.handle.net/10220/13495
ISSN: 0162-8828
DOI: 10.1109/TPAMI.2011.220
Rights: © 2012 IEEE
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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