Temporal segmentation of video using frame and histogram space

Joyce, Robert A.; Liu, Bede

doi:10.1109/tmm.2005.861285

Cited by 65 publications

(28 citation statements)

References 18 publications

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“…Another measure, the value [34], is used to evaluate the combination of recall and precision: (9) If , the recall and precision values are both 1. Since our algorithm can also predict intervals of transitions, we need another measure to evaluate the correctness of each predicted interval of a gradual transition.…”

Section: A Test Set and Resultsmentioning

confidence: 99%

Shot Change Detection via Local Keypoint Matching

Huang

Lee

Chen

2008

IEEE Trans. Multimedia

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Abstract-Shot change detection is an essential step in video content analysis. However, automatic shot change detection often suffers from high false detection rates due to camera or object movements. To solve this problem, we propose an approach based on local keypoint matching of video frames. This approach aims to detect both abrupt and gradual transitions between shots without modeling different kinds of transitions. Our experiment results show that the proposed algorithm is effective for most kinds of shot changes.

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Section: A Test Set and Resultsmentioning

confidence: 99%

Shot Change Detection via Local Keypoint Matching

Huang

Lee

Chen

2008

IEEE Trans. Multimedia

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“…This paper is only focused on the detection of gradual transitions. Different approaches have been proposed to extract the shots defined by gradual transitions, using different algorithms and models for these phenomena (Meng et al, 1995;Yeo and Liu, 1995;Demarty and Beucher, 1999;Fernando et al, 1999;Lu et al, 1999;Truong et al, 2000;Joyce and Liu, 2006). However, the results are not completely satisfactory even for very complex techniques.…”

Section: Introductionmentioning

confidence: 99%

Gradual Transition Detection for Video Partitioning Using Morphological Operators

Naranjo¹,

Angulo²,

Albiol³

et al. 2011

Image Anal Stereol

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Temporal segmentation of video data for partitioning the sequence into shots is a prerequisite in many applications: automatic video indexing and editing, old film restoration, perceptual coding, etc. The detection of abrupt transitions or cuts has been thoroughly studied in previous works. In this paper we present a scheme to identify the most common gradual transitions, i.e., dissolves and wipes, which relies on mathematical morphology operators. The approach is restricted to fast techniques which require low computation (without motion estimation and adapted to compressed sequences) and are able to cope with random brightness variations (often occurring in old films). The present study illustrates how the morphological operators can be used to analyze temporal series for detecting particular events, either working directly on the 1D signal or building an intermediate 2D image from the 1D signals to take advantage of the spatial operators.

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“…In order to represent the video's structure in a simpler style, we generally need to segment it into some logical related shots and assume that the segmentation is predefined by means of boundary identification [6,10,11]. Each segmented shot is called group of frames (GOFs).…”

Section: Introductionmentioning

confidence: 99%

Data indexing for video shot mining based on optical flow

Chen

et al. 2008

5th International Conference on Visual Information Engineering (VIE 2008)

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Summarizing and understanding video shot based on their contents is an important research topic in multimedia data mining. This paper presents an efficient algorithm based on optical flow field to mine the motion data contained in a given video shot. Two features called magnitude of motion pixel and direction of motion pixel are constructed respectively and adopted to split the video shot into some categories automatically.The corresponding indexical structure is extracted from each category and directly applied to motionbased queries. A test system has been developed to prove the validity of our algorithm. The experimental results show that the algorithm performs well and will play an important role in content-based video shot retrieval.

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Temporal segmentation of video using frame and histogram space

Cited by 65 publications

References 18 publications

Shot Change Detection via Local Keypoint Matching

Shot Change Detection via Local Keypoint Matching

Gradual Transition Detection for Video Partitioning Using Morphological Operators

Data indexing for video shot mining based on optical flow

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