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Content related to "clip-detection"

(1) Video Indexing and Retrieval in Compressed Domain Using Fuzzy-Categorization

There has been an increased interest in video indexing and retrieval in recent years. In this work, indexing and retrieval system of the visual contents is based on feature extracted from the compressed domain. Direct possessing of the compressed domain spares the decoding time, which is extremely important when indexing large number of multimedia archives. A fuzzycategorizing structure is designed in this paper to improve the retrieval performance. In our experiment, a database that consists of basketball videos has been constructed for our study. This database includes three categories: fullcourt match, penalty and close-up. First, spatial and temporal feature extraction is applied to train the fuzzy membership functions using the minimum entropy optimal algorithm. Then, the max composition operation is used to generate a new fuzzy feature to represent the content of the shots. Finally, the fuzzy-based representation becomes the indexing feature for the content-based video retrieval system. The experimental results show that the proposal algorithm is quite promising for semantic-based video retrieval.

(1) An Effective and Fast Scene Change Detection Algorithm for MPEG Compressed Videos

In this paper, we propose an effective and fast scene change detection algorithm directly in MPEG compressed domain. The proposed scene change detection exploits the MPEG motion estimation and compensation scheme by examining the prediction status for each macro-block inside B frames and P frames. As a result, locating both abrupt and dissolved scene changes is operated by a sequence of comparison tests, and no feature extraction or histogram differentiation is needed. Therefore, the proposed algorithm can operate in compressed domain, and suitable for real-time implementations. Extensive experiments illustrate that the proposed algorithm achieves up to 94% precision for abrupt scene change detection and 100% for gradual scene change detection. In comparison with similar existing techniques, the proposed algorithm achieves superiority measured by recall and precision rates.

(1) COMPRESSED-DOMAIN SHOT BOUNDARY DETECTION USING FINITE STATE MACHINE AND CONTENT-BASED RULES

We propose a fast and systematic method for shot boundary detection in compressed domain using content-based rules and FSM (finite state machine). Firstly, several feature indicators are acquired from DC images in MPEG videos including luminance, color, edge, prediction error and inter-frame difference as well as motion. Then, several content-based rules are utilized to detect abrupt cuts. Thirdly, boundaries of gradual transitions are determined by a coarse to fine procedure with a pre-processing module and a FSM. According to the experiments using publicly available sequences from TRECVID, the results have showed that the proposed algorithm outperforms the representative existing algorithms in both precision rate and recall rates.

(1) Description of Online and Offline Metadata Extraction out of Sports Videos

We focus on online and offline metadata extraction and annotation out of sports videos. The main benefit of our method is immediate and automatic extraction and annotation of metadata by giving semantics to combinations of heterogeneous low-level visual features. It brings new opportunities for efficient utilisation of sports video in improved ways, and is easily customized to address the characteristics. Firstly, semantic scene classification is described, including key-frames extraction, similarities determination between shots, and rule based estimation of scene boundaries. Secondly, fuzzy logic based categorizing is presented, including paradigm, Fuzzy membership function, and fuzzy feature generation and similarity measure. Thirdly, automatic sports video annotation is proposed, including robust dominant colour region detection, combined motion feature analysis. This work has been evaluated in the TRECVID 2007 competition.

(1) Camera Motion Analysis towards Semantic-based Video Retrieval in Compressed Domain

To reduce the semantic gap between low-level visual features and the richness of human semantics, this paper proposes new algorithms, by virtue of the combined camera motion descriptors with multi-threshold, to automatically retrieve the semantic concepts, i.e., close-up, and panorama, directly in MPEG compressed domain based on camera motion analysis. Extensive experiments illustrate that the proposed algorithms provide promising retrieval results under real-time application scenario and without human intervention

(1) Real-time and Automatic Close-up Retrieval from Compressed Videos

In this paper, we propose a thorough scheme, by virtue of camera zooming descriptor with two-level threshold, to automatically retrieve close-ups directly from MPEG compressed videos based on camera motion analysis. In the retrieval process, we build camera-motion-based semantic retrieval. To improve the coverage of the proposed scheme, we investigate close-up retrieval in all kinds of videos. Extensive experiments illustrate that the proposed scheme provides promising retrieval results under real-time and automatic application scenario.

(1) Real-time shot cut detection in compressed domain

In this short paper, we propose a fast and simple shot cut detection algorithm, which directly operates in compressed domain and suitable for real-time implementation. The proposed algorithm exploits the existing MPEG techniques by examining the prediction status for each macro-block inside B frames and P frames. As a result, locating both abrupt and dissolved shot cuts is operated by a sequence of comparison tests, and thus no feature extraction or histogram differentiation is needed. Although the description of the algorithm is primarily based on MPEG-1 and MPEG-2 streams, the scheme can be readily extended to other video compression standards such as MPEG-4 and H.264 by following the principle on monitoring: (i) balance between forward prediction and backward prediction; and (ii) boundaries among P, B and I frames. Extensive experiments illustrate that the proposed algorithm outperforms similar existing algorithm, providing a useful technique for fast and on-line video content processing.

(1) Analysis of cluttered scenes using an elastic matching approach for stereo images.

We present a system for the automatic interpretation of cluttered scenes containing multiple partly occluded objects in front of unknown, complex backgrounds. The system is based on an extended elastic graph matching algorithm that allows the explicit modeling of partial occlusions. Our approach extends an earlier system in two ways. First, we use elastic graph matching in stereo image pairs to increase matching robustness and disambiguate occlusion relations. Second, we use richer feature descriptions in the object models by integrating shape and texture with color features. We demonstrate that the combination of both extensions substantially increases recognition performance. The system learns about new objects in a simple one-shot learning approach. Despite the lack of statistical information in the object models and the lack of an explicit background model, our system performs surprisingly well for this very difficult task. Our results underscore the advantages of view-based feature constellation representations for difficult object recognition problems.

(1) DCT-Domain Image Retrieval Via Block-Edge-Patterns

A new algorithm for compressed image retrieval is proposed in this paper based on DCT block edge patterns. This algorithm directly extract three edge patterns from compressed image data to construct an edge pattern histogram as an indexing key to retrieve images based on their content features. Three feature-based indexing keys are described, which include: (i) the first two features are represented by 3-D and 4-D histograms respectively; and (ii) the third feature is constructed by following the spirit of run-length coding, which is performed on consecutive horizontal and vertical edges. To test and evaluate the proposed algorithms, we carried out two-stage experiments. The results show that our proposed methods are robust to color changes and varied noise. In comparison with existing representative techniques, the proposed algorithms achieves superior performances in terms of retrieval precision and processing speed.

(1) Afuzzy logic approach for detection of video shot boundaries

Video temporal segmentation is normally the first and important step for content-based video applications. Many features including the pixel difference, colour histogram, motion, and edge information etc. have been widely used and reported in the literature to detect shot cuts inside videos. Although existing research on shot cut detection is active and extensive, it still remains a challenge to achieve accurate detection of all types of shot boundaries with one single algorithm. In this paper, we propose a fuzzy logic approach to integrate hybrid features for detecting shot boundaries inside general videos. The fuzzy logic approach contains two processing modes, where one is dedicated to detection of abrupt shot cuts including those short dissolved shots, and the other for detection of gradual shot cuts. These two modes are unified by a mode-selector to decide which mode the scheme should work on in order to achieve the best possible detection performances. By using the publicly available test data set from Carleton University, extensive experiments were carried out and the test results illustrate that the proposed algorithm outperforms the representative existing algorithms in terms of the precision and recall rates.
2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

(1) Object recognition with deformable feature graphs

A fundamental question in invariant object recognition is that of representation. This chapter reviews object representation based on deformable graphs that describe particular views of an object as a spatial constallation of image features. These representations are particular useful in situations of high clutter and partial occlusions. We demonstrate the benfits of these representations in three recognition applications: face analysis, hand gesture recognition and the interpretation of cluttered scenes composed of mutible partly occluded objects. We conclude by discussing current trends and open challenges.

(1) Radio Relief: Radio Archives Departments Benefit from Digital Audio Processing

The archives departments of radio broadcasters are currently facing two significant challenges, namely, how to store rapidly increasing amounts of radio content, and how to satisfy the rising demand for easy retrieval of audio clips that can be recycled into new programs. A pilot project demonstrates that digital audio processing techniques have the potential to provide much-needed support.