Image classification by combining local and global features

Several techniques have recently been proposed to extract the features of an image. Feature extraction is one of the most important steps in various image processing and computer vision applications such as image retrieval, image classification, matching, object recognition. Relevant feature (global or local) contains discriminating information and is able to distinguish one object from others. Global features describe the entire image, whereas local features describe the image patches (small group of pixels). In this paper, we present a novel descriptor to extract the color-texture features via two information types. Our descriptor named concatenation of local and global color features is based on the fusion of global features using wavelet transform and a modified version of local ternary pattern, whereas, for the local features, speeded-up robust feature descriptor and bag of words model were used. All the features are extracted from the three color planes. To evaluate the effectiveness of our descriptor for image classification, we carried out experiments using the challenging datasets: New-BarkTex, Outex-TC13, Outex-TC14, MIT scene, UIUC sports event, Caltech 101 and MIT indoor scene. Experimental results showed that our descriptor outperforms the existing state-of-the-art methods.