Novel color, shape and texture-based scene image descriptors

Document Type

Conference Proceeding

Publication Date

12-31-2012

Abstract

This paper introduces several novel color, shape and texture-based image descriptors for scene image classification with applications to image search and retrieval. Specifically, first, a new 3-Dimensional Local Binary Pattern (3D-LBP) descriptor is proposed for color image local feature extraction. Second, a new shape descriptor (HaarHOG) is introduced by combining Haar wavelet transformation and Histogram of Oriented Gradients (HOG). Third, these descriptors are fused using an optimal feature representation technique to generate a robust 3-Dimensional LBP-HaarHOG (3DLH) descriptor that can perform well on different scene image categories. Finally, the Enhanced Fisher Model (EFM) is applied for discriminatory feature extraction and the nearest neighbor classification rule is used for image classification. The proposed descriptors and fusion technique are evaluated using three grand challenge datasets: the MIT Scene dataset, the UIUC Sports Event dataset, and a part of the Caltech 256 dataset. © 2012 IEEE.

Identifier

84871596449 (Scopus)

ISBN

[9781467329514]

Publication Title

Proceedings 2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing Iccp 2012

External Full Text Location

https://doi.org/10.1109/ICCP.2012.6356193

First Page

245

Last Page

248

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