A PERCEPTUAL APPROACH FOR SEMANTIC IMAGE RETRIEVAL

The rapid growth of digital imaging technology and the accumulation of large collections of digital images has created the need for efficient and intelligent schemes for image classification and retrieval. Since humans are the ultimate users of most retrieval systems, it is important to organize the contents semantically, according to meaningful categories. We propose a novel approach for assigning semantic labels to image segments, which together segment layout information can lead to content-based image classification and retrieval. The proposed approach relies on a perceptually based, spatially adaptive, color-texture segmentation scheme. We derive segment-wide features (color and spatial texture). These features serve as medium level descriptors that can effectively bridge the "semantic gap" between low and high level descriptors. The segment classification into semantic categories is based on linear discriminant analysis techniques. We demonstrate the effectiveness of the proposed approach on a database that includes 5000 segments from approximately 2000 photographs of natural scenes