Taking advantage of motif matrix inference for rotated image indexing and retrieval

With the rapid development of information technology, the sizes of digital libraries become larger and larger. How to quickly and effectively search the desired images in huge digital libraries becomes the challenge needed to resolve with high priority. In this study, we firstly propose two motif-based matrices, i.e., the motif average matrix (MAM) and motif excessive matrix (MEM), to describe the color and texture features of an image. Subsequently, in terms of the inference of MAM and MEM, a motif matrix (MM) is further proposed to index rotated images and resolve the issue of rotated image retrieval. That is, in the light of such an inference, MM incorporates the color and texture characters and reveals the consistent relevance between the original and rotated images, which can be effectively used for rotated image retrieval. To extensively test the performance of our method, we carry out the experiments on the benchmark Corel image dataset, Brodatz texture image dataset, WIPO global brand dataset, and the experimental results show that our approach of motif matrix inference improves the retrieval performance in comparison with the state-of-the-art image retrieval approaches.