Local mesh quantized extrema patterns for image retrieval

In this paper, we propose a new feature descriptor, named local mesh quantized extrema patterns (LMeQEP) for image indexing and retrieval. The standard local quantized patterns collect the spatial relationship in the form of larger or deeper texture pattern based on the relative variations in the gray values of center pixel and its neighbors. Directional local extrema patterns explore the directional information in 0°, 90°, 45° and 135° for a pixel positioned at the center. A mesh structure is created from a quantized extrema to derive significant textural information. Initially, the directional quantized data from the mesh structure is extracted to form LMeQEP of given image. Then, RGB color histogram is built and integrated with the LMeQEP to enhance the performance of the system. In order to test the impact of proposed method, experimentation is done with bench mark image repositories such as MIT VisTex and Corel-1k. Avg. retrieval rate and avg. retrieval precision are considered as the evaluation metrics to record the performance level. The results from experiments show a considerable improvement when compared to other recent techniques in the image retrieval.