Comparative analysis of color histogram and LBP in CBIR systems

In the current trend, the image retrieval (IR) system has also been shifted from traditional text-based to content-based with the advancement in technology. Many issues have been resolved by content-based IR. In a particular case of information retrieval/image retrieval (IR) systems, the number of images on the web and the usage of images are growing exponentially. Therefore, IR system needs to be scalable, flexible, modularizable and promotes reusability so that it becomes easy to deploy, develop and maintain the system. In this paper, we have presented a CBIR model using Color Histogram and Local Binary Pattern (LBP) where both are built with Microservices architecture using docker platform. The framework used in this model is logic independent therefore any CBIR system can be run using this framework. The CBIR using Color Histogram uses chi-squared distance as a similarity measure while CBIR model using LBP is implemented using Linear Support Vector Machines for image classification. In our experiments, we have achieved the average recall, precision, and F-measure using Color Histogram 22.25%, 63.12%, and 32.67%, respectively. Though, we have achieved the average recall, precision, and F-measure using LBP 77.15%, 79.90%, and 76.17%, respectively. It has been observed that LBP model is more accurate than Color Histogram for detecting different weather conditions. It has also been found that the use of Microservices architecture leads to improve the non-functional qualities of a CBIR system as compared to traditional architecture styles by a great margin.