Novel color HWML descriptors for scene and object image classification
Sugata Banerji, Atreyee Sinha, Chengjun Liu · 2012
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.