PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

PicSOM Experiments in TRECVID 2011
Mats Sjöberg, Satoru Ishikawa, Markus Koskela and Jorma Laaksonen
In: TRECVID 2011 Workshop, 5-7 Dec 2011, Gaithersburg, MD.


Our experiments in TRECVID 2011 include participation in the semantic indexing and known-item search tasks. In the semantic indexing task we implemented linear and SVM-based classifiers on different low-level visual features extracted from the keyframes. In addition to the main keyframes provided by NIST, we also extracted and analysed additional frames from longer shots. The classifiers were fused using standard and weighted geometric mean. We submitted to the full task the following four runs: PicSOM_1: Weighted geometric mean of all linear and SVM classifiers. PicSOM_2: Weighted geometric mean of the SVM classifiers. PicSOM_3: Geometric mean of the linear classifiers. PicSOM_4: Geometric mean of all linear and SVM classifiers. The run PicSOM_1 obtained the highest MXIAP score of 0.1355. In the known-item search task we submitted four automatic runs: PicSOM_1: Text search with OCR and spell check + concept detectors PicSOM_2: Text search with OCR and spell check PicSOM_3: Text search with lemmatization + concept detectors PicSOM_4: Text search with lemmatization Our automatic runs used text search with a single video-level index containing all the ASR text plus the title, description and subjects from the meta data. We also included text detected by OCR and tried lemmatization in some runs. In addition we used automatic selection of concepts based on matching keywords in the query text. Neither the concept detectors nor the lemmatization managed to improve over our best run which was PicSOM_2 with a MIR score of 0.2720.

EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Multimodal Integration
Information Retrieval & Textual Information Access
ID Code:9122
Deposited By:Markus Koskela
Deposited On:21 February 2012