Image Retrieval based on Multi-features using Fuzzy Set

In content-based image retrieval (CBIR), the visual features of the database images are extracted, and the visual feature database is assessed in order to find the images closest to the query image. Increasing the efficiency and decreasing both the time and storage space of indexed images is the priority in developing the image retrieval systems. In this research work, an efficient system is proposed for image retrieval by applying fuzzy techniques, which are advantageous in increasing the efficiency and decreasing the length of the feature vector and storage space. The effect of increasing the considered content features count is assessed to enhance the image retrieval efficiency. The fuzzy features consist of color, statistical information related to the spatial dependency of the pixels on each other, and the position of image edges. These features are indexed in fuzzy vector format 16, 3, and 16 lengths. The extracted vectors are compared through the fuzzy similarity measures, where the most similar images are retrieved. In order to evaluate the proposed system’s performance, this system and three other non-fuzzy systems where fewer features are of concern are implemented. These four systems are tested on a database containing 1000 images, and the results obtained indicate improvement in the retrieval precision and storage space.