An Active Learning Based Approach For Effective Video Annotation And Retrieval

Autor:	Chatterjee, Moitreya, Leuski, Anton
Rok vydání:	2015
Předmět:	Computer Science - Multimedia Computer Science - Information Retrieval Computer Science - Learning H.3.3 H.5.1
Druh dokumentu:	Working Paper
Popis:	Conventional multimedia annotation/retrieval systems such as Normalized Continuous Relevance Model (NormCRM) [16] require a fully labeled training data for a good performance. Active Learning, by determining an order for labeling the training data, allows for a good performance even before the training data is fully annotated. In this work we propose an active learning algorithm, which combines a novel measure of sample uncertainty with a novel clustering-based approach for determining sample density and diversity and integrate it with NormCRM. The clusters are also iteratively refined to ensure both feature and label-level agreement among samples. We show that our approach outperforms multiple baselines both on a recent, open character animation dataset and on the popular TRECVID corpus at both the tasks of annotation and text-based retrieval of videos. Comment: 5 pages, 3 figures, Compressed version published at ACM ICMR 2015
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1504.07004 Zobrazit plný text záznamu View this record from Arxiv