Performing content-based image retrieval using rotated local binary pattern and multiple descriptors
Images have been viewed as a powerful medium for displaying visual information in numerous applications. Content-based retrieval and querying the indexed collections are required to access visual information. Content image retrieval is an efficient means of retrieving similar images from large repositories which require high speed and accuracy. The extraction of features is imperative for efficient results. Following research work aimed at colors and texture extraction for enhancing image metadata. The proposed technique was evaluated on WANG datasets using MATLAB. WANG consists of one thousand images of flowers, buildings and forest. Each category consists of one hundred images of size 187x126 or 126x187 in JPEG format. The features were extracted using RGB and HSV. Rotated Local Binary Patterns were used and distances between images was obtained using Euclidean algorithm. The results showed high accuracy and precision.