International audienceContent-Based Image Retrieval (CBIR) is the process of searching digital images in a large database based on features, such as colour, texture and shape of a given query image. As many images are compressed by transforms, constructing the feature vector directly in transform domain is a very popular topic. Therefore features can be extracted directly from images in compressed format by using, for example, Discrete Cosine Transform (DCT) for JPEG compressed images. This paper proposes a new Region-Based Image Retrieval approach using a Shape Adaptive Discrete Cosine Transform (SA-DCT). In this retrieval system, an image has a prior segmentation alpha plane, which is defined exactly as in MPEG-4. Therefore, an image is represented by segmented regions, and each region is associated with a feature vector derived from DCT and SA-DCT coefficients. So we can select any region as the main theme of the query image. For those images without distinctive objects and scenes, users can still select the whole image as the query condition. The experimental results show that the proposed approach is able to identify main objects and reduce the influence of background in the image, and thus improve the performance of image retrieval in comparison with a conventional Content-Based Image Retrieval based on DCT