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Proceedings Paper

SENSC algorithm for object and scene categorization
Author(s): Bao-di Liu; Yu-Jin Zhang
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Paper Abstract

So far, the most popular method for object & scene categorization (such as Vector Quantization (VQ), Sparse coding (SC)) transforms low-level descriptors (usually SIFT descriptors) into mid-level representations with more meaningful information. These methods have two key steps: (1) the building dictionary step, which provides a mechanism to map low-level descriptors into mid-level representations. (2) The coding step, which implements the map from low-level descriptors to mid-level representation for each image by the dictionary. In this paper, we proposed to use a stable and efficient nonnegative sparse coding (SENSC) algorithm for building dictionary and coding each image with it to develop an extension of Spatial Pyramid Matching (SPM) method. We also compare SENSC with SC (state-of-the-art performance) method and VQ method, analysis the drawbacks of SC and VQ for building dictionary, and show SENSC algorithm's performance. According to the experiments on three benchmarks (Caltech101, scene, and events), the method we proposed has shown a better performance than SC and VQ methods.

Paper Details

Date Published: 17 February 2011
PDF: 9 pages
Proc. SPIE 7881, Multimedia on Mobile Devices 2011; and Multimedia Content Access: Algorithms and Systems V, 78810R (17 February 2011); doi: 10.1117/12.872220
Show Author Affiliations
Bao-di Liu, Tsinghua Univ. (China)
Yu-Jin Zhang, Tsinghua Univ. (China)

Published in SPIE Proceedings Vol. 7881:
Multimedia on Mobile Devices 2011; and Multimedia Content Access: Algorithms and Systems V
David Akopian; Cees G. M. Snoek; Nicu Sebe; Reiner Creutzburg; Lyndon Kennedy, Editor(s)

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