A novel image retrieval method based on multi-trend structure descriptor

• An image feature representation method is proposed in this paper.
• Several feature maps and pixel changes are considered for image representation.
• The performance comparisons are made on Corel and Caltech datasets.

This paper proposes an image feature representation method, namely Multi-Trend Structure Descriptor (MTSD), which is built based on the local and multi-trend structures. The local structures can be regarded as the basic units for image analysis, and the multi-trend structures are introduced to explore the correlation among pixels in local structures according to the information change of pixels. The visual information such as color, edge orientation and intensity map are considered and quantized, and with the local structure as a bridge, we use multi-trend to detect color, edge orientation and intensity map respectively for feature extraction. MTSD can characterize not only the low-level features, such as color, shape and texture, but also the local spatial structure information. We evaluate the performance of the proposed algorithm on Corel and Caltech datasets, and experimental results demonstrate that, MTSD significantly outperforms texton co-occurrence matrix, multi-texton histogram, micro-structure descriptor and saliency structure histogram.