A content-based image retrieval system for hat database

Content-based image retrieval (CBIR) is a technique for indexing and retrieving images based on the low-level features, middle-level features, and high-level features. Low-level feature is extracted from contents of the images such as color, texture and shape; middle-level feature is a region obtained as a result of image segmentation; high-level feature is semantic information about the meaning of image, its objects and their roles, and categories to which the image belongs. In this project, three low-level features texture-based retrieval, color-based retrieval and shape-based retrieval are implemented and compared on hat database. Texture features are obtained from parameters of a two-component Gaussian mixture model (GMM) in the wavelet domain. Color features are extracted from a two-component GMM on HLS color space. Shape features are extracted from the contour by using centroid-contour distance Fourier descriptor. A comprehensive experimental evaluation of the retrieval performance of different feature sets is performed. The experimental results indicate that the shape features based on the centroid-contour distance Fourier descriptor perform much better than the color and texture features for the hat database used in this project