Novel color HWML descriptors for scene and object image classification

Document Type

Conference Proceeding

Publication Date

12-1-2012

Abstract

Several new image descriptors are presented in this paper that combine color, texture and shape information to create feature vectors for scene and object image classification. In particular, first, a new three dimensional Local Binary Patterns (3D-LBP) descriptor is proposed for color image local feature extraction. Second, three novel color HWML (HOG of Wavelet of Multiplanar LBP) descriptors are derived by computing the histogram of the orientation gradients of the Haar wavelet transformation of the original image and the 3D-LBP images. Third, the Enhanced Fisher Model (EFM) is applied for discriminatory feature extraction and the nearest neighbor classification rule is used for image classification. Finally, the Caltech 256 object categories database and the MIT scene dataset are used to show the feasibility of the proposed new methods. © 2012 IEEE.

Identifier

84875855514 (Scopus)

ISBN

[9781467325837]

Publication Title

2012 3rd International Conference on Image Processing Theory Tools and Applications Ipta 2012

External Full Text Location

https://doi.org/10.1109/IPTA.2012.6469564

First Page

330

Last Page

335

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