A successively refinable wavelet-based representation for content-based image retrieval

Content based retrieval of image and video data from databases is a very challenging problem, whose interest is dedved from the need of future databases to support efficient access to vast amounts of visual information. Typical queries to be performed in this context check attributes of objects present in image data, such as shape, color, relative locations, etc. Therefore, the way in which image data is represented plays a fundamental role in the efficient implementation of those queries. One possibility is to take the naive approach of storing images using standard compression techniques, storing image features (such as object shape descriptors, color histograms, etc.) as explicit side information, and whenever an image is involved in the evaluation of a query decoding it to full resolution; however, much more efficient techniques (in terms of storage and computational requirements) are possible. In this paper, we propose a new image coding technique which combines a wavelet image representation, embedded coding of the wavelet coefficients, and segmentation of semantically meaningful objects in the wavelet domain, to generate a bitstream in which each object is encoded independently of every other object in the image, and without explicitly storing shape boundary information. Furthermore, since the representation of each object is fully embedded applications may, independently for each object, specify the desired target bitrate and retrieve bits from the compressed bitstream. Preliminary results show that our new proposed method achieves PSNR numbers within 0.3dB of those achieved using the same coder without including segmentation information (which is one of the best within its class), thus showing that no severe performance loss results from enabling independent access to objects in the compressed domain.