Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Foundations and Trends in Information Retrieval
Modeling and representing events in multimedia
MM '11 Proceedings of the 19th ACM international conference on Multimedia
There is no data like less data: percepts for video concept detection on consumer-produced media
Proceedings of the 2012 ACM international workshop on Audio and multimedia methods for large-scale video analysis
Name that room: room identification using acoustic features in a recording
Proceedings of the 20th ACM international conference on Multimedia
International Journal of Multimedia Data Engineering & Management
Content-Based Multimedia Retrieval Using Feature Correlation Clustering and Fusion
International Journal of Multimedia Data Engineering & Management
E-LAMP: integration of innovative ideas for multimedia event detection
Machine Vision and Applications
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Given the exponential growth of videos published on the Internet, mechanisms for clustering, searching, and browsing large numbers of videos have become a major research area. More importantly, there is a demand for event detectors that go beyond the simple finding of objects but rather detect more abstract concepts, such as "feeding an animal" or a "wedding ceremony". This article presents an approach for event classification that enables searching for arbitrary events, including more abstract concepts, in found video collections based on the analysis of the audio track. The approach does not rely on speech processing, and is language-indepent, instead it generates models for a set of example query videos using a mixture of two types of audio features: Linear-Frequency Cepstral Coefficients and Modulation Spectrogram Features. This approach can be used in complement with video analysis and requires no domain specific tagging. Application of the approach to the TRECVid MED 2011 development set, which consists of more than 4000 random "wild" videos from the Internet, has shown a detection accuracy of 64% including those videos which do not contain an audio track.