Content-Dependent Image Search System for Aggregation of Color, Shape and Texture Features

The existing image search system often faces difficulty in finding an appropriate retrieved image corresponding to an image query. The difficulty is commonly caused by the users’ intention for searching image is different with dominant information of the image collected from feature extraction. In this paper, we present a new approach for the content-dependent image search system. The system utilizes information of color distribution inside an image and detects a cloud of clustered colors as something supposed as an object. We apply segmentation of an image as a content-dependent process before feature extraction in order to identify is there any object or not inside an image. The system extracts 3 features, which are color, shape, and texture features and aggregates these features for similarity measurement between an image query and image database. HSV histogram color is used to extract the color feature of the image. While the shape feature extraction used Connected Component Labeling (CCL) which is calculated the area value, equivalent diameter, extent, convex hull, solidity, eccentricity, and perimeter of each object. The texture feature extraction used Leung Malik (LM)’s approach with 15 kernels. For applicability of our proposed system, we applied the system with benchmark 1000 image SIMPLIcity dataset consisting of 10 categories namely Africans, beaches, buildings historians, buses, dinosaurs, elephants, roses, horses, mountains, and food. The experimental results performed 62% accuracy rate to detect objects by color feature, 71% by texture feature, 60% by shape feature, 72% by combined color-texture feature, 67% by combined color-shape feature, 72 % combined texture-shape features and 73% combined all features.