Content-Adaptive Video Summarization Combining Queueing and Clustering

Liu, Tiecheng; Katpelly, Ravi

doi:10.1109/icip.2006.312380

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…It uses color based key frame extraction technique but takes account of activities in the video for determining the number of key frames to be represented in the summary. [15] extracts key frames based on a three-step algorithm. The three major steps are preprocessing, temporal filtering and post processing.…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Video Summarization

Geetha¹,

Narayanan²

2014

IJIES

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Most previous work on summarization targeted either only on video or on its description of single video document. To efficiently manage huge video documents, this paper addresses a new prototype for summarizing video documents that automatically processes text and key-frames from the given video documents, indexes and provides mechanism for search and retrieval. The system separately summarizes the text and video related information. Both summarizations will enhance the performance as well the quality of the video document retrieval processes. The text summarizer provides a summarized view of a large video document to improve the learning speed and to give a glimpse of the document for better understanding. The video summarization consists of the key frames that effectively express the content and context of the video. This is achieved by quantifying visual attention of each frame in a video. This framework provides meaningful two-way summarization for video documents.

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Section: Introductionmentioning

confidence: 99%

Multi-Modal Video Summarization

Geetha¹,

Narayanan²

2014

IJIES

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“…The efficiency of the retrieval process depends on how video shots, scenes, and events are organized in the video database [1]. Several papers in the literature [2][3][4][5][6] have proposed strategies for segmenting video into separate video shots around which a database can be organized.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Video Shot Segmentation Using Global Color and Texture Information

Chang

Lee

Hong

et al. 2008

Advances in Visual Computing

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Abstract. This paper presents an effective algorithm to segment color video into shots for video indexing or retrieval applications. This work adds global texture information to our previous work, which extended the scale-invariant feature transform (SIFT) to color global texture SIFT (CGSIFT). Fibonacci lattice-quantization is used to quantize the image and extract five color features for each region of the image using a symmetrical template. Then, in each region of the image partitioned by the template, the entropy and energy of a cooccurrence matrix are calculated as the texture features. With these global color and texture features, we adopt clustering ensembles to segment video shots. Experimental results show that the additional texture features allow the proposed CGTSIFT algorithm to outperform our previous work, fuzzy-c means, and SOM-based shot detection methods.

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“…They first cluster and then concatenate short video segments of key frames to construct a video skimming. In [6], Liu and Katpelly provided a content-adaptive video summarization with key frame selection. As video is composed with continuous frames which contain meaningful information, it is difficult to represent the video structure and help users to understand the video content exactly using only key frame comparison.…”

Section: Introductionmentioning

confidence: 99%

Shot-based similarity measure for content-based video summarization

Gao

Dai

2008

2008 15th IEEE International Conference on Image Processing

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The rapid development of multimedia applications over the past decade requires efficient methods for video browsing. In this paper, we present an algorithm for video summarization with shot comparison. We analyze video content in the shot level, and we calculate the shot distance using the advanced Hausdorff distance. The advanced Hausdorff distance combines the Hausdorff distance and Boolean model, and it could compare two shots from the global view. When the shot similarity matrix is obtained, we group these video shots into several clusters using the affinity propagation cluster method to remove redundant video content. Performance evaluation on ten video sequences are given to illustrate the proposed algorithm.

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Content-Adaptive Video Summarization Combining Queueing and Clustering

Cited by 4 publications

References 10 publications

Multi-Modal Video Summarization

Multi-Modal Video Summarization

Unsupervised Video Shot Segmentation Using Global Color and Texture Information

Shot-based similarity measure for content-based video summarization

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