Dominant Texture Descriptors for image classification and retrieval

In this paper, we propose a generic approach for representing image texture features in a compact and intuitive way. Our approach, called Dominant Texture Descriptor (DTD), is inspired by the dominant color descriptor. It is based on clustering the local texture features and identifying the dominant components and their spatial distribution. We also present an enhanced version of the DTD (eDTD) that encodes the spatial distribution of the pixels within each dominant component. We illustrate this approach for the case of two well-known descriptors, namely, the MPEG-7 Edge Histogram, and Ga- bor texture. The performance of the proposed texture feature representation is illustrated by using it to classify a collection of 900 color images. Experimental results are compared with those obtained using the traditional approaches. We show that our representation is more compact, interpretable, and could improve classification results by 10%-20%, especially for images with non-homogeneous texture.