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Key-frame extraction and object segmentation are usually implemented independently and separately due to the fact that they are on different semantic levels and involve different features. In this work, we propose a joint key-frame extraction and object segmentation method by constructing a unified feature space for both processes, where key-frame extraction is formulated as a feature selection process for object segmentation in the context of Gaussian mixture model (GMM)-based video modeling. Specifically, two divergence-based criteria are introduced for key-frame extraction. One recommends key-frame extraction that leads to the maximum pairwise interclass divergence between GMM components. The other aims at maximizing the marginal divergence that shows the intra-frame variation of the mean density. The proposed methods can extract representative key-frames for object segmentation, and some interesting characteristics of key-frames are also discussed. This work provides a unique paradigm for content-based video analysis. [ABSTRACT FROM AUTHOR] more...

Video segmentation has been an important and challenging issue for many video applications. Usually there are two different video segmentation approaches, i.e., shot-based segmentation that uses a set of key-frames to represent a video shot and object-based segmentation that partitions a video shot into objects and background. Representing a video shot at different semantic levels, two segmentation processes are usually implemented separately or independently for video analysis. In this paper, we propose a new approach to combine two video segmentation techniques together. Specifically, a combined key-frame extraction and object-based segmentation method is developed based state-of-the-art video segmentation algorithms and statistical clustering approaches. On the one hand, shot-based segmentation can dramatically facilitate and enhance object-based segmentation by using key-frame extraction to select a few key-frames for statistical model training. On the other hand, object-based segmentation can be used to improve shot-based segmentation results by using model-based key-frame refinement. The proposed approach is able to integrate advantages of these two segmentation methods and provide a new combined shot-based and object-based frame- work for a variety of advanced video analysis tasks. Experimental results validate effectiveness and flexibility of the proposed video segmentation algorithm. [ABSTRACT FROM AUTHOR] more...

Focuses on the proposal of post processing method for low bit-rate wavelet-based image coding. Use of wavelet modulus maximum representation; Development of compensation functions to restore the distorted wavelet modulus maxima; Improvements in visual quality and image fidelity. more...

Proposes a novel post-processing method for low bit-rate wavelet-based image coding for edge reconstruction. Edge model in original images; Edge detection and parameter estimation; Edge degradation due to image compression; Model parameter estimation in the compressed signal; Edge-reconstruction algorithm. more...