Content-Based Image Retrieval using Local Patterns and Supervised Machine Learning Techniques

CBIR is a very important domain, especially in the last decade due to the increased need for image retrieval from the multimedia database. In general, we extract low level (color, texture, and shape) or high-level features (when we include machine learning techniques) from the images. In our work, we proposed a new CBIR system using Local Neighbor Pattern (LNP) with supervised machine learning techniques. We evaluated the performance of this system by comparing the system with Local Tetra Pattern (LTrP) using Corel 1k, Vistex and TDF face databases. We used three types of the database (i.e color, texture and face databases) to improve the effectiveness of our system. Performance analysis shows that LNP gives better performance regarding the average recall than LBP, LDP, and LTrP. To increase the accuracy of this system we used the LNP method with machine learning techniques and performance analysis shows that local pattern with machine learning techniques improves the average accuracy from 36.23% to 85.60% when we use LNP with cubic SVM on DB1 (Corel1K), and from 82.51% to 99.50 % when we use LNP with fine KNN on DB2 (Vistex DB), and from 56.63% to 95% when we use LNP with ensemble subspace discriminant on DB3 (face DB).