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论文类型：期刊论文

发表时间：2016-01-01

发表刊物：IEEE ACCESS

收录刊物：SCIE、EI、Scopus

卷号：4

页面范围：6204-6215

ISSN号：2169-3536

关键字：Internet of Things; feature extraction; image retrieval; image representation; local descriptor

摘要：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.