ECL-LIRIS at TrecVid 2011: Semantic Indexing

This is the first time that our team participate TRECVID. This paper summarizes our approach submitted to Semantic Indexing (SIN) task in TRECVID 2011. Our approach adopts bag-of-features method to transform original visual and audio features into histogram features, using pre-trained codebook. After feature transformation, one-versus-others SVMs with Chi-square kernel are trained. In decision step, averaged probability is calculated as a final score to rank shots. Under this framework, we tested 4 visual features including dense grid SIFT, color SIFT, OLBPC and DAISY together with 1 audio feature consisting of MFCC with delta and acceleration. Our audio visual combination model achieves best results in terms of mean xinfAP. Besides, considering the huge amount of data this year, we employed several speedup strategies such as k-means clustering with GPU acceleration and homogeneous kernel map. All these efforts rank us at the 12 th out of 19 teams in full run and the 13 th out of 27 teams in the light run test.