Ka-Man Wong

MIRROR: an interactive content based image retrieval system

A content based image retrieval system, called MPEG-7 image retrieval refinement based on relevance feedback (MIRROR), is developed for evaluating MPEG-7 visual descriptors and developing new retrieval algorithms. The system core is based on MPEG-7 experimentation mode (XM) with Web-based user interface for query by image example retrieval. A new merged color palette approach for MPEG-7 dominant color descriptor similarity measure and relevance feedback are also developed in this system. Several MPEG-7 visual descriptors are adopted in MIRROR for performance comparison purposes.

Novel Directional Gradient Descent Searches for Fast Block Motion Estimation

Search point pattern-based fast block motion estimation algorithms provide significant speedup for motion estimation but usually suffer from being easily trapped in local minima. This may lead to low robustness in prediction accuracy particularly for video sequences with complex motions. This problem is especially serious in one-at-a-time search (OTS) and block-based gradient descent search (BBGDS), which provide very high speedup ratio. A multipath search using more than one search path has been proposed to improve the robustness of BBGDS but the computational requirement is much increased. To tackle this drawback, a novel directional gradient descent search (DGDS) algorithm using multiple OTSs and gradient descent searches on the error surface in eight directions is proposed in this letter. The search point patterns in each stage depend on the minima found in these eight directions, and thus the global minimum can be traced more efficiently. In addition, a fast version of the DGDS (FDGDS) algorithm is also described to further improve the speed of DGDS. Experimental results show that DGDS reduces computation load significantly compared with the well-known fast block motion estimation algorithms. Moreover, FDGDS can achieve faster speedup compared with the UMHexagonS algorithm in H.264/AVC implementation while maintaining very similar rate-distortion performance.

Dominant color image retrieval using merged histogram

A merged histogram method (MHM) is proposed for histogram-based image retrieval based on dominant colors in images. In MHM, colors from images and between images are merged to form their dominant color set, instead of color components. Images of similar color can thus be selected and ranked. Experimental results show that MHM can achieve up to 32% and up to 22% more precise than RGB-based histogram and perceptually weighted histogram methods, respectively.

A new palette histogram similarity measure for MPEG-7 dominant color descriptor

The MPEG-7 dominant color descriptor (DCD) is an effective, compact, and intuitive representation of salient colors in an image region and is very useful for content-based image retrieval and indexing applications. However, the original quadratic histogram distance measure (QHDM) for MPEG-7 DCD similarity matching does not fit the human perception very well. It may cause incorrect ranks between regions with similar salient color distributions. In this paper, a new merged palette histogram similarity measure is proposed to provide an alternative for the QHDM. The new similarity measure is based on a merged color palette as a common color space to define histogram intersection. Experimental results show that this new similarity measure provide better accurate and perceptually relevant image retrieval for MPEG-7 DCD.

Novel Point-Oriented Inner Searches for Fast Block Motion Estimation

Recently, an enhanced hexagon-based (EHS) search algorithm was proposed to speedup the original hexagon-based search (HS) using a 6-side-based fast inner search. However, this 6-side-based method is quite irregular by inspecting the distance between the inner search points and the coarse search points that would lower prediction accuracy. In this paper, a new point-oriented grouping strategy is proposed to develop fast inner search techniques for speeding up the HS and diamond search (DS) algorithms. Experimental results show that the new HS and DS using point-oriented inner searches are faster than their original algorithms up to 30% with negligible peak signal-to-noise ratio degradation

A Search Patterns Switching Algorithm for Block Motion Estimation

Center-biased fast motion estimation algorithms, e.g., block-based gradient descent search and diamond search, can perform much better than coarse-to-fine search algorithms, such as 2-D logarithmic search and three-step search. The latter type of algorithms, however, is more suitable for handling large motion content. To combine the advantages of both types of algorithms, an adaptive algorithm performing search patterns switching (SPS) is proposed in this paper. The proposed SPS algorithm classifies the motion content of a block using a simple yet efficient motion content classifier called error descent rate. Unlike other classifiers with heavy overhead, this classifier requires only the searching of a few points in the search window and then a division operation. Experimental results show that the proposed SPS algorithm is very robust.

Dominant Color Structure Descriptor for Image Retrieval

A new dominant color structure descriptor (DCSD) is proposed in this paper. It is designed to provide an efficient way to represent both color and spatial structure information with single compact descriptor. The descriptor combines the compactness of dominant color descriptor (DCD) and the retrieval accuracy of color structure descriptor (CSD) to enhance the retrieval performance in a highly efficient manner. The feature extraction and similarity measure of the descriptor are designed to address the problems of the existing descriptors while utilize the advantages of them. Experimental results show that DCSD has a significant improvement on both retrieval performance and descriptor size over DCD. An eight-color DCSD (DCSD 8) gives an averaged normalized modified retrieval rate (ANMRR) of 0.0993 using MPEG-7 common color dataset, outperforming compact configurations of scalable color descriptor and color structure descriptor with smaller descriptor size.

Merged-color histogram for color image retrieval

Conventional histogram-based image retrieval algorithms usually find only intersecting areas of the color-component distributions of images, and thus work well in matching images with exact colors instead of similar colors, especially for computer generated pictures. This could be greatly affected by overall variations, such as intensity changes. A novel merged-color histogram (MCH) method for color image retrieval is proposed to facilitate matching between similar colors by means of color quantization and palette merging. Color quantization compacts the color information and matches each color instead of color components, and matching of similar colors is accomplished using palette merging. Experimental results show that the proposed MCH method is about 11-32% more precise in the first 20 retrievals for the same image query, and is able to recall 14%-23% more relevant images in the first 100 retrievals, as compared to the conventional RGB-based histogram method.

A foreground biased depth map refinement method for DIBR view synthesis

The performance of view synthesis using depth image based rendering (DIBR) highly depends on the accuracy of depth map. Inaccurate boundary alignment between texture image and depth map especially for large depth discontinuities always cause annoying artifacts in disocclusion regions of the synthesized view. Pre-filtering approach and reliability-based approach have been proposed to tackle this problem. However, pre-filtering approach blurs the depth map with drawback of degradation of the depth map and may also cause distortion in non-hole region. Reliability-based approach uses reliable warping information from other views to fill up holes and is not suitable for the view synthesis with single texture video such as video-plus-depth based DIBR applications. This paper presents a simple and efficient depth map preprocessing method with use of texture edge information to refine depth pixels around the large depth discontinuities. The refined depth map can make the whole texture edge pixels assigned with foreground depth values. It can significantly improve the quality of the synthesized view by avoiding incorrect use of foreground texture information in hole filling. The experimental results show the proposed method achieves superior performance for view synthesis by DIBR especially for large baseline.

A novel weighted cross prediction for H.264 intra coding

In this paper, a novel weighted cross prediction (WCP) mode is proposed to replace DC mode in Intra_4×4 prediction of H.264/AVC. In the proposed scheme, the upper right part of one 4×4 block mainly employs vertical prediction while the lower left part mainly uses horizontal prediction, predicting both in vertical and horizontal directions in one block. This scheme uses simple prediction equations with fixed weighting coefficients. Experimental results show that WCP has improvement compared to H.264 and it is very competitive while comparing to other Intra_4×4 prediction algorithms.