Paper Info
Title
Characteristic pattern discovery in videos
Abstract
In this paper, we present an approach to discover characteristic patterns in videos. We characterize the videos based on frequently occurring patterns like scenes, characters, sequence of frames in an unsupervised setting. With our approach, we are able to detect the representative scenes and characters of movies. We also present a method for detecting video stop-words in broadcast news videos based on the frequency of occurrence of sequence of frames. These are analogous to stop-words in text classification and search. We employ two different video mining schemes; both aimed at detecting frequent and representative patterns. For one of our mining approaches, we use an efficient frequent pattern mining algorithm over a quantized feature space. Our second approach uses a Random Forest to first represent video data as sequences, and then mine the frequent patterns. We validate the proposed approaches on broadcast news videos and our database of 81 Oscar winning movies.
Year
DOI
Venue
2010
10.1145/1924559.1924600
ICVGIP
Keywords
Field
DocType
characteristic pattern discovery,video stop-words,mining approach,video data,efficient frequent pattern mining,representative pattern,broadcast news video,frequent pattern,different video mining scheme,representative scene,feature space,random forest
Data mining,Broadcasting,Feature vector,Pattern recognition,Computer science,Artificial intelligence,Data mining algorithm,Random forest,Video mining
Conference
Citations 
PageRank 
References 
0
0.34
19
Authors
2
Name
Order
Citations
PageRank
Mihir Jain141616.37
C. V. Jawahar21700148.58