Robust 3D Clue-Based Video Segmentation for Video Indexing

Ardebilian, M.; Chen, L.; Tu, Xiaowei

doi:10.1006/jvci.1998.0407

Cited by 5 publications

(6 citation statements)

References 27 publications

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“…Several authors [11,24] propose a learning procedure in order to use an appropriate threshold value.…”

Section: Notationsmentioning

confidence: 99%

“…Considering a number of operations per pixel respectively equal to at least 4 for equation (9), 6 for equation (10), 3 for equation (11), and 2 for equation (14) or (15), the overall complexity is then equal to O(15P). Lawrence et al [49] use evolution of temporal derivative of the pixel intensities as a criterion for shot change detection.…”

Section: Pixel Intensity Time Variationmentioning

confidence: 99%

“…Ardebilian et al [11] detect shot changes by comparing between feature points extracted from two successive images. They use Hough transform to extract feature points.…”

Section: Feature Pointsmentioning

confidence: 99%

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A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval

2003

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We present in this paper a review of methods for segmentation of uncompressed video sequences. Video segmentation is usually performed in the temporal domain by shot change detection. In case of real-time segmentation, computational complexity is one of the criteria which has to be taken into account when comparing different methods. When dealing with uncompressed video sequences, this criterion is even more significant. However previous published reviews did not involve complexity criterion when comparing shot change detection methods. Only recognition rate and ability to classify detected shot changes were considered. So contrary to previous reviews we give here complexity of most of the described methods. We review in this paper an extensive set of methods presented in the literature and classify them in several parts, depending on the information used to detect shot changes. The earliest methods were comparing successive frames by relying on most simple elements, that is to say pixels. Comparison could be performed on a global level, so methods based on histograms were also proposed. Block-based methods have been considered to process data at an intermediate level, between local (using pixels) and global (using histograms) levels. More complex features can be involved, resulting in feature-based methods. Alternatively some methods rely on motion as a criterion to detect shot changes. Finally different kinds of information could be combined together in order to increase the quality of shot change detection. So our review will detail segmentation methods

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“…Several authors [11,24] propose a learning procedure in order to use an appropriate threshold value.…”

Section: Notationsmentioning

confidence: 99%

Section: Pixel Intensity Time Variationmentioning

confidence: 99%

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A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval

2003

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“…Zabih et al [3] proposed to obtain the similarity between frames by analyzing the edges of consecutive images. There are other approaches of this category as the work of Ardebilian et al [4] which is based on comparing characteristic points or the approach proposed by Porter et al [5] who use the correlation between frames.…”

Section: Introduction and Previous Workmentioning

confidence: 98%

Color Video Segmentation by Dissimilarity Based on Edges

Ramos

Novo

Rouco

et al. 2010

Lecture Notes in Computer Science

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Abstract. In this work new approaches are proposed to the extension to color-space of different shot change detection methods. These techniques are those ones that use the dissimilarity based on edges, in particular on space and frequency domains. They were previously defined to deal with grayscale videos, so the methods are redesigned to provide the best possible results in color-space videos. Moreover, some improvements are performed to obtain better results, as the use of an adaptative threshold instead of the fixed one previously used. Some experiments are presented to show the better behaviour of the new developed approaches.

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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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Robust 3D Clue-Based Video Segmentation for Video Indexing

Cited by 5 publications

References 27 publications

A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval

A review of real-time segmentation of uncompressed video sequences for content-based search and retrieval

Color Video Segmentation by Dissimilarity Based on Edges

Semantics-Based Video Indexing using a Stochastic Modeling Approach

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