Indexing of Image Features Using Quadtree

Abstract

In Content based image retrieval (CBIR) applications, the idea of indexing is mapping the extracted descriptors from images into a high-dimensional space. In this paper, visual features like color, texture and shape are considered. The color features are extracted using color coherence vector (CCV), texture features are obtained from Segmentation based Fractal Texture Analysis (SFTA). The shape features of an image are extracted using the Fourier Descriptors (FD) which is the contour based feature extraction method. All features of an image are then combined. After combining the color, texture and shape features using appropriate weights, the quadtree is used for indexing the images. It is experimentally found that the proposed indexing method using quadtree gives better performance than the other existing indexing methods.