This paper presents a novel architecture of MPEG-4 zerotree encoder. Under the architecture, a fast technique of label coefficients is proposed to reduce the recursive scan. It is achieved by exploiting the feature of the MPEG-4 zerotree symbol alphabet. A new combined structure of ZTR address buffer and the significant flag bit is described. It can simplify the skipping of the significant coefficients and locating the descendent coefficients of ZTR/VZTR. Furthermore, a preprocessor is given for independent encoding of each tree in individual bitplane.

We present a novel scheme for content-based video retrieval by exploring the spatio-temporal information. A shot with significant content changes can be segmented into several subshots that are of coherent content, and a shot similarity measure for video retrieval can be computed from the similarity between corresponding subshots. To characterize the temporal content variations in one shot, we developed two descriptors: dominant color histograms (DCH) and spatial structure histograms (SSH). By fusing temporal information into color content, DCHs for a "group of frames" (GoF) are trying to capture the dominant colors with long durations, which would be the colors of the focused objects or background. SSH is a set of features extracted from color-blob maps to describe spatial information for one individual frame. Experimental results on real-world sports videos prove that our proposed approach achieves the best performance on the average recall (AR) and average normalized modified retrieval rank (ANMRR) for video shot retrievals.

Texture analysis plays an important role in image analysis and pattern recognition. Texture-based modelling has become a powerful tool for content-based image retrieval. A new texture feature named LFH is proposed based on the local-similarity and 8-neighbour gray-tone spatial dependencies by using local Fourier transform. We extracted a histogram of local Fourier coefficients maps of the texture image for texture classification and texture retrieval. Experiments on 13 samples of Brodatz textures demonstrate the efficiency and effect of texture classification. The performance of texture retrieval is illustrated.

In this paper, we present a novel scheme for automatic video scene extraction. A pseudo-object-based shot representation containing more semantics is proposed to measure shot similarity and an force competition approach is proposed to group shots into scene based on content coherences between shots. Two content descriptors, color objects: Dominant Color Histograms (DCH) and Spatial Structure Histograms (SSH), are introduced. To represent temporal content variations, a shot can be segmented into several subshots that are of coherent content, and shot similarity measure is formulated as subshot similarity measure. With this shot representation, scene structure can be extracted by analyzing the splitting and merging force competitions at each shot boundary. Experiment on MPEG-7 test videos achieves promising results by the proposed algorithm. 1.

A color transform is necessary for better color image coding. In this paper, we concentrate on a comparative study of color transforms for color image coding in order to find the best one among 11 published color transforms: YCrCb, NTSC, PAL, HDTV, WW, XYZ, DCT, DHT, two approximate K-L transforms (K1K2K3 and KLT) and the original reversible color transform (ORCT) adopted in JPEG-2000. Experiment results with JPEG-2000 verification model (VM5.1) are sorted by color transforms and counted up for all the test images and diverse bit rates. The sorting scoring table shows that the discrete cosine transform performs best among 11 tested color transform at 6 lossy bit rates for all the 2 JPEG-2000 color test images and other 23 color images we used. Then we derive an integer reversible transform of DCT and an approximate implementation using additions and shifts only for both lossless and lossy color image coding. Experiments with the integer reversible color transforms show that the proposed transform scheme is better than ORCT for lossy image coding.

In this paper, we describe a new way of representing a symbolic picture by a two-dimensional string. A picture query can also be specified as a 2-D string. The problem of pictorial information retrieval then becomes a problem of 2-D subsequence matching. We present algorithms for encoding a symbolic picture into its 2-D string representation, reconstructing a picture from its 2-D string representation, and matching a 2-D string with another 2-D string. We also prove the necessary and sufficient conditions to characterize ambiguous pictures for reduced 2-D strings as well as normal 2-D strings. This approach thus allows an efficient and natural way to construct iconic indexes for pictures.

In this paper, we suggest a class of (attributed) expansive graph grammars which generate languages contained in a graph family ¿. It turns out that by means of node renumbering using a very effi-cient algorithm, any graph in ¿ can be converted into a standard form, which enables the use of related string representation for that graph to facilitate the syntax analysis. As a consequence, the syntax analysis of (attributed) expansive graph language is very efficient and almost like the parsing of tree languages. Furthermore, a syntax-directed transla-tion can be established for mapping one (attributed) expansive graph language to another. Finally, since many relational graphs for scene analysis can be considered as belonging to these graph languages, the proposed graph grammar model appears to be quite attractive from the application point of view.