Re-ranking by Local Re-scoring for Video Indexing and Retrieval

Bahjat Safadi (1) , Georges Quénot (2)

1 LIG - Laboratoire d'Informatique de Grenoble
2 MRIM - Modélisation et Recherche d’Information Multimédia [Grenoble]

Video retrieval can be done by ranking the samples according to their probability scores that were predicted by classifiers. It is often possible to improve the retrieval performance by re-ranking the samples. In this paper, we proposed a re-ranking method that improves the performance of semantic video indexing and retrieval, by re-evaluating the scores of the shots by the homogeneity and the nature of the video they belong to. Compared to previous works, the proposed method provides a framework for the re-ranking via the homogeneous distribution of video shots content in a temporal sequence. The experimental results showed that the proposed re-ranking method was able to improve the system performance by about 18\% in average on the TRECVID 2010 semantic indexing task, videos collection with homogeneous contents. For TRECVID 2008, in the case of collections of videos with non-homogeneous contents, the system performance was improved by about 11-13\%.

Mots clés: Video Indexing and Retrieval; Re-ranking

Domaines: Recherche d'information [cs.IR]

Bahjat Safadi, Georges Quénot. Re-ranking by Local Re-scoring for Video Indexing and Retrieval. CIKM 2011 - International Conference on Information and Knowledge Management, Oct 2011, Glasgow, United Kingdom. pp.2081-2084, ⟨10.1145/2063576.2063895⟩. ⟨hal-00763624⟩