Quaero at TRECVID 2011: Semantic Indexing and Multimedia Event Detection

The Quaero group is a consortium of French and German organizations working on Multimedia Indexing and Retrieval 1. LIG and KIT participated to the semantic indexing task and LIG participated to the organization of this task. LIG also participated to the multimedia event detection task. This paper describes these participations. For the semantic indexing task, our approach uses a sixstages processing pipelines for computing scores for the likelihood of a video shot to contain a target concept. These scores are then used for producing a ranked list of images or shots that are the most likely to contain the target concept. The pipeline is composed of the following steps: descriptor extraction, descriptor optimization, classification, fusion of descriptor variants, higher-level fusion, and re-ranking. We used a number of different descriptors and a hierarchical fusion strategy. We also used conceptual feedback by adding a vector of classification score to the pool of descriptors. The best Quaero run has a Mean Inferred Average Precision of 0.1529, which ranked us 3rd out of 19 participants. We participated to the multimedia event detection task with a system derived from the generic one we have for general purpose concept indexing in videos considering the target events as concepts. Detection scores on videos are produced from the scores on shots.

Quaero at TRECVID 2012: Semantic Indexing

International audienceThe Quaero group is a consortium of French and German organizations working on Multimedia Indexing and Retrieval. LIG, INRIA and KIT participated to the semantic indexing task and LIG participated to the organization of this task. This paper describes these participations. For the semantic indexing task, our approach uses a six-stages processing pipelines for computing scores for the likelihood of a video shot to contain a target concept. These scores are then used for producing a ranked list of images or shots that are the most likely to contain the target concept. The pipeline is composed of the following steps: descriptor extraction, descriptor optimization, classi cation, fusion of descriptor variants, higher-level fusion, and re-ranking. We used a number of di erent descriptors and a hierarchical fusion strategy. We also used conceptual feedback by adding a vector of classi cation score to the pool of descriptors. The best Quaero run has a Mean Inferred Average Precision of 0.2692, which ranked us 3rd out of 16 participants. We also organized the TRECVid SIN 2012 collaborative annotation