Wicket Fall Concept Mining From Cricket Video Using A-Priori Algorithm

Goyani, Mahesh; Dutta, Shreyash; Gohil, Gunvatsinh; Naik, Sapan

doi:10.5121/ijma.2011.3110

Cited by 10 publications

(4 citation statements)

References 17 publications

(19 reference statements)

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“…In addition to SVM, mining rules for classification has been applied in the fields of image and video retrieval (Ballan, Bertini, Del Bimbo, & Serra, 2010;Bouziani, Goita, & He, 2010), especially in object detection (Kleban, Xie, & Ma, 2008;Perko, Paletta, & Leonardis, 2009), event detection (Anwar & Naftel, 2008; and concept detection (Fatemi, Poulin, Raileany, & Smeaton, 2009;Goyani, Dutta, Gohil, & Naik, 2011;Liu, Weng, Tseng, Chuang, & Chen, 2008;Vijayakumar & Nedunchezhian, 2013), taking advantages of its ability to handle large databases with a high accuracy (Bouzouita & Elloumi, 2007). In (Kleban, Xie, & Ma, 2008), the Apriori algorithm was used to capture the frequent spatial configurations of quantized local features for logo detection.…”

Section: Related Workmentioning

confidence: 99%

“…However, our proposed framework is to discover the cooccurrence among features and each concept. In Goyani, Dutta, Gohil, and Naik (2011), A-priori algorithm was used for mining the higher-level concept in the hierarchical tree. Key-frames, real-time and replay frames, field view, pitch view and non field view in real-time frames, close up and crowd frames, player of teams and player's gathering of teams were detected sequentially at five levels in the first stage; and the labels of each frame event are labeled using A-priori algorithm for concept mining in the second stage.…”

Section: Related Workmentioning

confidence: 99%

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Rule-Based Semantic Concept Classification from Large-Scale Video Collections

Lin

Shyu

Chen

2013

International Journal of Multimedia Data Engineering and Management

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The explosive growth and increasing complexity of the multimedia data have created a high demand of multimedia services and applications in various areas so that people can access and distribute the data easily. Unfortunately, traditional keyword-based information retrieval is no longer suitable. Instead, multimedia data mining and content-based multimedia information retrieval have become the key technologies in modern societies. Among many data mining techniques, association rule mining (ARM) is considered one of the most popular approaches to extract useful information from multimedia data in terms of relationships between variables. In this paper, a novel rule-based semantic concept classification framework using weighted association rule mining (WARM), capturing the significance degrees of the feature-value pairs to improve the applicability of ARM, is proposed to deal with major issues and challenges in large-scale video semantic concept classification. Unlike traditional ARM that the rules are generated by frequency count and the items existing in one rule are equally important, our proposed WARM algorithm utilizes multiple correspondence analysis (MCA) to explore the relationships among features and concepts and to signify different contributions of the features in rule generation. To the authors best knowledge, this is one of the first WARM-based classifiers in the field of multimedia concept retrieval. The experimental results on the benchmark TRECVID data demonstrate that the proposed framework is able to handle large-scale and imbalanced video data with promising classification and retrieval performance.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Rule-Based Semantic Concept Classification from Large-Scale Video Collections

Lin

Shyu

Chen

2013

International Journal of Multimedia Data Engineering and Management

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“…Then crowd frames classified into spectators, player's gathering of team A or player's gathering of team B. In the second stage, labels are associated with each frame event, which is used as input to A-Priori algorithm for concept mining [26]. Lin Lin, Guy Ravitz, Mei-Ling Shyu, and Shu-Ching Chen extracted multimodal audio and visual features from the broadcast and used the apriori algorithm.…”

Section: Concept Detectionmentioning

confidence: 99%

Recent Trends and Research Issues in Video Association Mining

Vijayakumar¹,

Nedunchezhian²

2011

IJMA

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With the ever-growing digital libraries and video databases, it is increasingly important to understand and mine the knowledge from video database automatically. Discovering association rules between items in a large video database plays a considerable role in the video data mining research areas. Based on the research and development in the past years, application of association rule mining is growing in different domains such as surveillance, meetings, broadcast news, sports, archives, movies, medical data, as well as personal and online media collections. The purpose of this paper is to provide general framework of mining the association rules from video database. This article is also represents the research issues in video association mining followed by the recent trends.

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“…Chen and Shyu [14] proposed a hierarchical temporal association mining approach that integrates the association rule mining and the sequential pattern discovery to systematically determine the temporal patterns for target events. Goyani et al [33] proposed an A-priori algorithm to detect the semantic concepts from the cricket video. Initially, a top-down event detection and classification was performed using the hierarchical tree.…”

Section: Video Association Miningmentioning

confidence: 99%

A study on video data mining

Vijayakumar

Nedunchezhian²

2012

Int J Multimed Info Retr

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Data mining is a process of extracting previously unknown knowledge and detecting the interesting patterns from a massive set of data. Thanks to the extensive use of information technology and the recent developments in multimedia systems, the amount of multimedia data available to users has increased exponentially. Video is an example of multimedia data as it contains several kinds of data such as text, image, meta-data, visual and audio. It is widely used in many major potential applications like security and surveillance, entertainment, medicine, education programs and sports. The objective of video data mining is to discover and describe interesting patterns from the huge amount of video data as it is one of the core problem areas of the data-mining research community. Compared to the mining of other types of data, video data mining is still in its infancy. There are many challenging research problems existing with video mining. Beginning with an overview of the video datamining literature, this paper concludes with the applications of video mining.

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Wicket Fall Concept Mining From Cricket Video Using A-Priori Algorithm

Cited by 10 publications

References 17 publications

Rule-Based Semantic Concept Classification from Large-Scale Video Collections

Rule-Based Semantic Concept Classification from Large-Scale Video Collections

Recent Trends and Research Issues in Video Association Mining

A study on video data mining

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