Image retrieval approach based on local texture information derived from predefined patterns and spatial domain information
This is an incremental improvement for image retrieval systems, potentially benefiting database management and computer vision applications.
The paper tackles image retrieval by combining local texture information from two descriptors, achieving higher precision than many known methods on the Simplicity database.
With the development of Information technology and communication, a large part of the databases is dedicated to images and videos. Thus retrieving images related to a query image from a large database has become an important area of research in computer vision. Until now, there are various methods of image retrieval that try to define image contents by texture, color or shape properties. In this paper, a method is presented for image retrieval based on a combination of local texture information derived from two different texture descriptors. First, the color channels of the input image are separated. The texture information is extracted using two descriptors such as evaluated local binary patterns and predefined pattern units. After extracting the features, the similarity matching is done based on distance criteria. The performance of the proposed method is evaluated in terms of precision and recall on the Simplicity database. The comparative results showed that the proposed approach offers higher precision rate than many known methods.