A feature-based algorithm for detecting and classifying production effects

Shot segmentation provides the basis for almost all high-level video content analysis approaches, validating it as one of the major prerequisites for efficient video semantic analysis, indexing and retrieval. The successful detection of both gradual and abrupt transitions is necessary to this end. In this paper a new gradual transition detection algorithm is proposed, that is based on novel criteria such as color coherence change that exhibit less sensitivity to local or global motion than previously proposed ones. These criteria, each of which could serve as a standalone gradual transition detection approach, are then combined using a machine learning technique, to result in a meta-segmentation scheme. Besides significantly improved performance, advantage of the proposed scheme is that there is no need for threshold selection, as opposed to what would be the case if any of the proposed features were used by themselves and as is typically the case in the relevant literature. Performance evaluation and comparison with four other popular algorithms reveals the effectiveness of the proposed technique.

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“…Recall Precision Standard Deviation of Pixel Intensities [2] 0.765 0.484 ECR [1] 0.654 0.125 EAG [3] 0 …”

Section: Methodsmentioning

confidence: 99%

“…In [1] transition detection is based on the analysis of intensity edges. An edge pixel that appears far from an existing edge pixel is defined as an entering pixel, while a previously existing edge pixel that disappears is defined as an exiting pixel.…”

Section: Introductionmentioning

confidence: 99%

Gradual transition detection using color coherence and other criteria in a video shot meta-segmentation framework

Tsamoura

Mezaris

Kompatsiaris

2008

2008 15th IEEE International Conference on Image Processing

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“…Much work exists in shot-cut detection [2,3]. Popular techniques that use image features [7], optical flow [14] etc., are not applicable due to the heavy noise that is common in broadcast videos. In such cases, a histogram based descriptor is well suited [4].…”

Section: Visual Domain Processing Of Videosmentioning

confidence: 99%

“…Previous work that segments a video into scenes [1,5] using visual features [6,7] or scene dynamism [8], fail in many cases where there is no significant visual change across space and time. This is especially true for the class of sports videos.…”

Section: Introductionmentioning

confidence: 99%

Text Driven Temporal Segmentation of Cricket Videos

Sankar

Pandey

Jawahar

2006

Computer Vision, Graphics and Image Processing

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Abstract. In this paper we address the problem of temporal segmentation of videos. We present a multi-modal approach where clues from different information sources are merged to perform the segmentation. Specifically, we segment videos based on textual descriptions or commentaries of the action in the video. Such a parallel information is available for cricket videos, a class of videos where visual feature based (bottom-up) scene segmentation algorithms generally fail, due to lack of visual dissimilarity across space and time. With additional topdown information from textual domain, these ambiguities could be resolved to a large extent. The video is segmented to meaningful entities or scenes, using the scene level descriptions provided by the commentary. These segments can then be automatically annotated with the respective descriptions. This allows for a semantic access and retrieval of video segments, which is difficult to obtain from existing visual feature based approaches. We also present techniques for automatic highlight generation using our scheme.

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“…Automatic temporal video segmentation methods usually involve computing pixel-level and/or histogram-based difference measures for each pair of successive frames in the video stream and then using shot boundary detection techniques to locate the positions of shot boundaries [2], [3]. More advanced temporal segmentation techniques use sophisticated image features such as edges [4], focus of expansion points [5] and image motion [6]. However, just as a phoneme can appear in many different words, visually similar video shots can appear in different video segments with different semantic meanings.…”

Section: Introductionmentioning

confidence: 99%

Semantics-Based Video Indexing using a Stochastic Modeling Approach

Wei

Bhandarkar

2007

2007 IEEE International Conference on Image Processing

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Semantic video indexing is the first step towards automatic video retrieval and personalization. We propose a data-driven stochastic modeling approach to perform both video segmentation and video indexing in a single pass. Compared with the existing Hidden Markov Model (HMM)-based video segmentation and indexing techniques, the advantages of the proposed approach are as follows: (1) the probabilistic grammar defining the video program is generated entirely from the training data allowing the proposed approach to handle various kinds of videos without having to manually redefine the program model; (2) the proposed use of the Tamura features improves the accuracy of temporal segmentation and indexing; (3) the need to use an HMM to model the video edit effects is obviated thus simplifying the processing and collection of training data and ensuring that all video segments in the database are labeled with concepts that have clear semantic meanings in order to facilitate semantics-based video retrieval. Experimental results on broadcast news video are presented.

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A feature-based algorithm for detecting and classifying production effects

Cited by 206 publications

References 12 publications

Gradual transition detection using color coherence and other criteria in a video shot meta-segmentation framework

Gradual transition detection using color coherence and other criteria in a video shot meta-segmentation framework

Text Driven Temporal Segmentation of Cricket Videos

Semantics-Based Video Indexing using a Stochastic Modeling Approach

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