Automatic detection of wipes and their frame ranges is important for the purpose of reliable video parsing and video database indexing. Wipes are difficult to detect because of the complexity and variety of the transition effects. Many of the existing wipe detection algorithms could detect only a few wipe effects. The false/miss detection problem caused by motion is also very serious. In this paper, we propose a novel wipe detection algorithm that can detect most wipe effects with accurate frame ranges. We carefully model a wipe based on its nature and then use the model to filter out possible confusion caused by motion or other transition effects. More precisely, properties of independence and completeness are proposed to characterize an ideal wipe; frame ranges of potential wipes are located by finding sequences which are a close approximation to an ideal wipe. Bayes rule is applied to each potential wipe to statistically estimate an adaptive threshold for the purpose of wipe verification. Experiment results on videos with different genres show that the proposed methodology can be used to detect various wipe effects effectively.Index Terms-Multimedia analysis, shot segmentation, video processing, wipe detection.

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The success of content-based image retrieval (CBIR) relies critically on the ability to find effective image features to represent the database images. The shape of an object is a fundamental image feature and belongs to one of the most important image features used in CBIR. In this article we propose a robust and effective shape feature known as the compound image descriptor (CID), which combines the Fourier transform (FT) magnitude and phase coefficients with the global features. The underlying FT coefficients have been shown analytically to be invariant to rotation, translation, and scaling. We also present details of the underlying innovative shape feature extraction method. The global features, besides being incorporated with the FT coefficients to form the CID, are also used to filter out the highly dissimilar images during the image retrieval process. Thus, they serve a dual purpose of improving the accuracy and hence the robustness of the shape descriptor, and of speeding up the retrieval process, leading to a reduced query response time. Experiment results show that the proposed shape descriptor is, in general, robust to changes caused by image shape rotation, translation, and/or scaling. It also outperforms other recently published proposals, such as the generic Fourier descriptor

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