Mining temporal patterns of movement for video content classification

Scalable approaches to video content classification are limited by an inability to automatically generate representations of events ode abstract temporal structure. This paper presents a method in which temporal information is captured by representing events using a lexicon of hierarchical patterns of movement that are mined from large corpora of unannotated video data. These patterns are then used as features for a discriminative model of event classification that exploits tree kernels in a Support Vector Machine. Evaluations show the method learns informative patterns on a 1450-hour video corpus of natural human activities recorded in the home. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning –knowledge acquisition. General Terms Algorithms, Experimentation. Keywords Temporal Data Mining, Video Content Classification, Video Event Recognition, Tree Kernel, Support Vector Machine.

• Type: Conference
• Year: 2006
• Where: MIR
• Authors: Michael Fleischman, Philip DeCamp, Deb Roy
• Keywords: MIR 2006, Multimedia, Support Vector Machine, Tree Kernel, Video Content Classification