Content-based multimedia information retrieval is an interesting but difficult area of research. Current approaches include the use of color, texture, and shape information. This paper proposes a novel approach to content-based image retrieval. In our retrieval system, an image is represented by a set of color histogram and edge histogram descriptors. The histogram Euclidean distance, cosine distance and histogram intersection are used to measure the image level similarity. Finally, the overall similarity is computed as a weighted combination of image similarity measures incorporating all features. Our proposed retrieval approach demonstrates a promising performance for an image database including 766 general-purpose images. Effectiveness is documented by experimental results.
Embedded block coding with optimized truncation (EBCOT) with the wavelet shape of tree encoding structure is more
flexible because it encodes each code block respectively by decomposing the subband into code blocks, so that the
embedded code streams will come into being to support the mass classification, the hierarchical resolution and the
random access. However the anti-missing performance via network of the algorithm is worse. The source signal has been
divided into many code streams by multi-description coding (MDC) of image and video so that it will be transferred
through the insecure transmission channel. Region of Interest (ROI) coding gives priority to the focus of doctor's interest
generally occupies lesser part of entire medical image. In this paper, ROI coding, which combines MDC and EBCOT,
has been done according to JPEG2000 ROI coding standard in medical images. The algorithm not only uses the
hierarchical spatial resolution and random access to ROI of EBCOT, but also has improved the anti-missing performance
via network, and formed robust code stream. The experimental results demonstrated that the coding method improved
the system compression ratio without influence on the medical diagnosis.