Dissolve transition detection algorithm using spatio-temporal distribution of MPEG macro-block types (poster session)

Jun, Sung-Bae; Yoon, Kyoungro; Lee, Hee-Youn

doi:10.1145/354384.354542

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…A shot can be defined as "a single sequence of frames in motion picture obtained by one camera without interruption" [2]. Connecting shot-level analysis with additional information allows indexing of the video content at the scene level [3].…”

Section: Introductionmentioning

confidence: 99%

Temporal video segmentation for real-time key frame extraction

Calic¹,

Sav²,

Izquierdo³

et al. 2002

IIEEE International Conference on Acoustics Speech and Signal Processing

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The extensive amount of media coverage today, generates difficulties in identifying and selecting desired information. Browsing and retrieval systems become more and more necessary in order to support users with powerful and easy-to-use tools for searching, browsing and summarization of information content. The starting point for these tasks in video browsing and retrieval systems is the low level analysis of video content, especially the segmentation of video content into shots. This paper presents a fast and efficient way to detect shot changes using only the temporal distribution of macroblock types in MPEG compressed video. The notion of a dominant reference frame is introduced here. A dominant frame denotes the reference frame (I or P) used as prediction reference for most of the macroblocks from a subsequent B frame.

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Section: Introductionmentioning

confidence: 99%

Temporal video segmentation for real-time key frame extraction

Calic¹,

Sav²,

Izquierdo³

et al. 2002

IIEEE International Conference on Acoustics Speech and Signal Processing

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“…Although the proposed approach is computationally intensive and cannot be targeted for real-time applications, the estimated dominant motion is a useful by-product that is readily to be utilized for motion characterization. In addition, Jun et al 42 proposed a fast and efficient dissolve detector by utilizing the macroblock information of MPEG videos. The authors empirically found the typical patterns of macroblocks during dissolves.…”

Section: Gradual Transition Detectionmentioning

confidence: 99%

Recent Advances in Content-Based Video Analysis

Ngo

Pong

Zhang³

2001

Int. J. Image Grap.

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In this paper, we present major issues in video parsing, abstraction, retrieval and semantic analysis. We discuss the success, the difficulties and the expectations in these areas. In addition, we identify important opened problems that can lead to more sophisticated ways of video content analysis.For video parsing, we discuss topics in video partitioning, motion characterization and object segmentation. The success in video parsing, in general, will have a great impact on video representation and retrieval. We present three levels of abstracting video content by scene, keyframe and key object representations. These representation schemes in overall serve as a good start for video retrieval. We then describe visual features, in particular motion, and similarity measures adopted for retrieval. Next, we discuss the recent computational approaches in bridging the semantic gap for video content understanding.

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“…There has been a tremendous amount of work in past decade on the scene change detection in the literature (Jun, Yoon, and Lee, 2000;Lu, Chang, and Yao, 1999;Meng, Juan, and Chang, 1995;Nagasaka and Tanaka, 1992;Patel and Sethi, 1996;Truong, Dorai, and Venkatesh, 2000;Yeo and Liu, 1995;Zabih, Miller and Mai, 1999;Zhang et al, 1993;Zhang, Qi, and Zhang, 2001). The earlier work concentrates mainly on the abrupt cut.…”

Section: Introductionmentioning

confidence: 99%

“…This study focuses primarily on identifying the existence of a transition rather than its precise temporal extent. The ratio of forward macro blocks in the B-type frames and the spatial distribution of forward/backward macro blocks is utilized for detecting dissolve changes in the researches of Jun, Yoon, and Lee (2000). In the researches of Lienhart (2001), novel dissolve detection algorithm using machine learning and multi-resolution concept is proposed.…”

Section: Introductionmentioning

confidence: 99%