Motion Pattern-Based Video Classification and Retrieval

Ma, Yufei; Zhang, Hong-Jiang

doi:10.1155/s1110865703211021

Cited by 24 publications

(15 citation statements)

References 18 publications

(19 reference statements)

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“…Videos can then be indexed based on either global or segmentation features. In [24], a motion pattern descriptor namely motion texture is proposed for video retrieval and the classification of simple camera and object motion patterns. In [8], spatio-temporal interactions between objects are expressed by predicate logic for video retrieval.…”

Section: Related Workmentioning

confidence: 99%

Video event detection using motion relativity and visual relatedness

Wang

Jiang

Ngo

2008

Proceedings of the 16th ACM International Conference on Multimedia

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Event detection plays an essential role in video content analysis. However, the existing features are still weak in event detection because: i) most features just capture what is involved in an event or how the event evolves separately, and thus cannot completely describe the event; ii) to capture event evolution information, only motion distribution over the whole frame is used which proves to be noisy in unconstrained videos; iii) the estimated object motion is usually distorted by camera movement. To cope with these problems, in this paper, we propose a new motion feature, namely Expanded Relative Motion Histogram of Bag-ofVisual-Words (ERMH-BoW) to employ motion relativity and visual relatedness for event detection. In ERMH-BoW, by representing what aspect of an event with Bag-of-VisualWords (BoW), we construct relative motion histograms between visual words to depict the object activities or how aspect of the event. ERMH-BoW thus integrates both what and how aspects for a complete event description. Instead of motion distribution features, local motion of visual words is employed which is more discriminative in event detection. Meanwhile, we show that by employing relative motion, ERMH-BoW is able to honestly describe object activities in an event regardless of varying camera movement. Besides, to alleviate the visual word correlation problem in BoW, we propose a novel method to expand the relative motion histogram. The expansion is achieved by diffusing the relative motion among correlated visual words measured by visual relatedness. To validate the effectiveness of the proposed feature, ERMH-BoW is used to measure video clip similarity with Earth Mover's Distance (EMD) for event detection. We conduct experiments for detecting LSCOM events in TRECVID 2005 video corpus, and performance is improved by 74% and 24% compared with existing motion distribution feature and BoW feature respectively.

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Section: Related Workmentioning

confidence: 99%

Video event detection using motion relativity and visual relatedness

Wang

Jiang

Ngo

2008

Proceedings of the 16th ACM International Conference on Multimedia

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“…At present, extensive research has been carried out and reported on video content analysis and various event detections. [1][2][3][4][5][6][7][8][9][10][11][12] Reference 1 reported a sports video classification technique via exploitation of the human vision system in perceiving some salient regions inside video frames, which are represented by regions of interests (ROI). The technique first extracts ROIs and then clusters these ROIs to extract color and texture features to classify sports videos.…”

Section: Introductionmentioning

confidence: 99%

Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

et al. 2010

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Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.) Abstract. We propose a support-vector-machine (SVM) tree to hierarchically learn from domain knowledge represented by low-level features toward automatic classification of sports videos. The proposed SVM tree adopts a binary tree structure to exploit the nature of SVM's binary classification, where each internal node is a single SVM learning unit, and each external node represents the classified output type. Such a SVM tree presents a number of advantages, which include: 1. low computing cost; 2. integrated learning and classification while preserving individual SVM's learning strength; and 3. flexibility in both structure and learning modules, where different numbers of nodes and features can be added to address specific learning requirements, and various learning models can be added as individual nodes, such as neural networks, AdaBoost, hidden Markov models, dynamic Bayesian networks, etc. Experiments support that the proposed SVM tree achieves good performances in sports video classifications. C 2010 Society of Photo-Optical Instrumentation Engineers.Optical Engineering 49(12), 127003 (December 2010

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“…SVM (Support vector machine) and HMM (Hidden Markov model) are examples of model-based classifiers [5,13,17]. However, recent work on image classification has shown that nearest neighbor based classifiers serve as fast classifiers which can provide good performance even with large datasets [4].…”

Section: Introductionmentioning

confidence: 99%

Video categorization using object of interest detection

Kowdle

Chang

Chen

2010

2010 IEEE International Conference on Image Processing

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Object of Interest (OOI) detection has been widely used in many recent works in video analysis, especially in video similarity and video retrieval. In this paper, we describe a generic video classification algorithm using object of interest dectection. We use online user-submitted videos and aim to categorize the videos into six broad categories hot star, news, anime, pets, sports and commercials. We show through our experiments that, detecting and describing the object of interest improves the video classification accuracy by about 10 percentage points.

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Motion Pattern-Based Video Classification and Retrieval

Cited by 24 publications

References 18 publications

Video event detection using motion relativity and visual relatedness

Video event detection using motion relativity and visual relatedness

Support-vector-machine tree-based domain knowledge learning toward automated sports video classification

Video categorization using object of interest detection

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