Kobe University and Muroran Institute of Technology at TRECVID 20112 Semantic Indexing Task.
Kimiaki Shirahama, Kuniaki Uehara · TRECVID · 2012
This paper describes our method developed for TRECVID 2012 Semantic INdexing (SIN) Task. Our main research purpose is the development of a fast method, which can work on a single processor with no performance degra- dation. To this end, computationally expensive processes are re-formulated based on matrix operation. We re-formulate the Euclidian distance computation for the kernel value computa- tion in an SVM, and the probability density computation of multivariate normal distributions for the GMM supervector representation. This enables accurate concept detection using a large number of training examples, and spatially-temporally dense features. The following four runs were submitted to SIN (light) task: L A kobe muro l5 4: This is our baseline run using five