A novel CBIR system is described that incorporates both high
level (semantic) and low level visual content. It is suitable for medical image information systems, to assist clinical diagnosis or for clinician training purposes. The system is XML-compliant and utilises MPEG-7 content descriptions and encoded metadata. The image retrieval process is driven by relevance feedback, enabling the indirect transferal of expert knowledge. A novel attribute visualisation facility enables the user to understand how the search
criteria are modified and the effectiveness of the guidance provided. The relevance feedback visualisation can be used also to re-sort retrieved results according to the user's requirements and permit the interactive investigation of pertinent features. The effectiveness of the system is demonstrated by two examples from the field of dermatology. Evaluations show that combining the attribute
visualisation with conventional retrieval techniques both increases
user confidence levels and provides additional system functionality.
We describe new motion coding algorithms that develop fixed size block matching (FSBM) into variable sized block matching (VSBM) and a modified approach (MVSBM) which can exploit irregularly shaped areas of uniform motion. New generations of video coding standards handle arbitrary shaped visual objects as well as frame based input. We explain how MVSBM strategies work when combining shaped data and block based algorithms. Locally accurate motion information is produced by the combination of otherwise ambiguous estimates produced by the small area matching required to detect locally diverse movements. The success of various prediction strategies indicates that the motion information is well behaved and thus likely to be accurate, given complex natural image sequence source material. MVSBM is evaluated by forcing it to perform with the same quality prediction as FSBM, then comparing the number of bits required by each technique. FSBM vector coding methods are taken from H.263 and MPEG-4 for comparison with those developed for MVSBM, extra compression phases are developed for MVSBM by utilizing the greater structure of the representation. Results are presented for three MPEG-4 test sequences, 'Container', 'weather' and 'Stefan'. We show bit savings of 67 percent for 'container', and 13 percent for 'Stefan' with more complex object activity.
We describe a quad-tree based variable size block matching (VSBM) motion estimation algorithm which is as computationally efficient as fixed size block matching (FSBM) and yet provides a better quality prediction. The 'match and merge' scheme allows the dimensions of blocks to adapt to local activity within the image, and the total number of blocks in any frame can be varied while still representing true motion fairly accurately. This permits adaptive it allocation between the representation of displacement and residual data, and the variation of the overall bit allowance on a frame-by-frame basis. The cost of coding the motion information from the VSBM technique is compared with the 2D motion vector prediction adopted by H.263 and MPEG-4 using FSBM with 16 by 16 macroblocks. 1D and 2D VSBM motion vector prediction strategies are described. The techniques are evaluated using two complete MPEG-4 test sequences. For similar quality prediction (same mean square error), 16 percent fewer bits are required to ode the motion vectors from the 'Foreman' sequence using the VSBM technique and a 2D predictor. The saving increases to 68 percent for the 'Container Ship' sequence in which there is less disparate motion. The cost of including the quad- tree description is included in both cases.
We report two techniques for variable size block matching (VSBM) motion compensation. Firstly an algorithm is described which, based on a quad-tree structure, results in the optimal selection of variable-sized square blocks. It is applied in a VSBM scheme in which the total mean squared error is minimized. This provides the best-achievable performance for a quad- tree based VSBM technique. Although it is computationally demanding and hence impractical for real-time codecs, it does provide a yardstick by which the performance of other VSBM techniques can be measured. Secondly, a new VSBM algorithm which adopts a `bottom-up' approach is described. The technique starts by computing sets of `candidate' motion vectors for fixed-size small blocks. Blocks are then effectively merged in a quad-tree manner if they have similar motion vectors. The result is a computationally-efficient VSBM technique which attempts to estimate the `true' motion within the image. Both methods have been tested on a number of real image sequences. In all cases the new `bottom-up' technique was only marginally worse than the optimal VSBM method but significantly better than fixed-size block matching and other known VSBM implementations.
In this investigation, motion estimation is carried out on three image sequences using a block matching approach. Each frame of the image sequence is partitioned into a number of fixed size blocks, and for each block the fractal dimension is calculated. For each block in the current frame, the best-matching block in the previous frame is identified using a novel two- pass searching scheme. In the first pass, the fractal dimension is calculated in nine positions within the search space. The coarse position of the corresponding block is identified based on the similarity of the fractal dimension. In the second pass, a grey level exhaustive search around the coarse position is used to determine the exact position of the corresponding block. The searching process is waived if the block has negligible movement. Preliminary results show that the new motion estimation method requires much less computation than the exhaustive search technique and provides a better estimate than the three-step search method, especially for large search spaces.