Texture element feature characterizations for CBIR

Colour and texture are the most common features used in CBIR systems today. In this paper, we wish to investigate structural methods of texture analysis for CBIR in view of their closeness to human perception and description of texture. In structural analysis, local patterns are the key (as is the case with humans), and when used as features may be expected to return more relevant images in CBIR. One method to describe local patterns in computationally simple terms is texture spectrum proposed by He and Wang. In this paper, we propose two additional characterizations of local patterns. The first is an extension of He and Wang´s texture spectrum to larger and more meaningful windows, along with new structural features that capture local patterns such as horizontal and vertical stripes, alternating dark and bright spots, etc. The second is a new method that characterizes patterns as contrast variations in 5 × 5 windows. We apply the new texture characterizations to develop a CBIR application and tested their performance on two databases containing remote sensing images. Our results show accuracies that range from 60% to 100% depending on the query image and the features contained therein. These results indicate that our texture features are useful in retrieving images appropriate for different remote sensing applications.