Novel color Gabor-LBP-PHOG (GLP) descriptors for object and scene image classification

This paper presents a novel set of color descriptors for object and scene image classification. We first introduce a new Gabor-PHOG (GPHOG) descriptor by concatenating the Pyramid of Histograms of Oriented Gradients (PHOG) of the local Gabor filtered images. Second, we derive the Gabor-LBP (GLBP) descriptor by accumulating the Local Binary Patterns (LBP) histograms of all the component images produced by applying Gabor filters. Then, by combining the GPHOG and the GLBP descriptors using an optimal feature representation method, we propose a novel Gabor-LBP-PHOG (GLP) image descriptor which performs well on different image categories. Next, we make a comparative assessment of the classification performance of the GLP descriptor in six different color spaces. Finally, we present a novel Fused Color GLP (FC-GLP) feature by integrating the PCA features of the six color GLP descriptors. The Principal Component Analysis (PCA) and the Enhanced Fisher Model (EFM) are applied for feature extraction and the nearest neighbor classification rule is used for classification. The effectiveness of the proposed GLP and FC-GLP feature vectors for image classification is evaluated using three grand challenge datasets, namely the Caltech 256 dataset, the MIT Scene dataset and the UIUC Sports Event dataset.