With the applications heterogeneous of Internet of Things (IoT) technology, the heterogeneous IoT systems generate a large number of heterogeneous datas, including videos and images. How to efficiently represent these images is an important and challenging task. As a local descriptor, the texton analysis has attracted wide attentions in the field of image processing. A variety of texton-based methods have been proposed in the past few years, which have achieved excellent performance. But, there still exists some problems to be solved, especially, it is difficult to describe the images with complex scenes from IoT. To address this problem, this paper proposes a multi-feature representation method called diagonal structure descriptor. It is more suitable for intermediate feature extraction and conducive to multi-feature fusion. Based on visual attention mechanism, five kinds of diagonal structure textons are defined by the color differences of diagonal pixels. Then, four types of visual features are extracted from the mapping sub-graphs and integrated into 1-D vector. Various experiments on three Corel-datasets demonstrate that the proposed method performs better than several state-of-the-art methods.
['L. Yu', 'Lin Feng', 'Chen Chen', 'Tie Qiu', 'Li Li', 'Jun Wu']
A Novel Multi-Feature Representation of Images for Heterogeneous IoTs