NDLI: Multi-Concept Multi-Modality Active Learning for Interactive Video Annotation

Content Provider	IEEE Xplore Digital Library
Author	Meng Wang Xian-Sheng Hua Yan Song Jinhui Tang Li-Rong Dai
Copyright Year	2007
Description	Author affiliation: Univ. of Sci. & Technol. of China, Hefei (Meng Wang)
Abstract	Active learning methods have been widely applied to reduce human labeling effort in multimedia annotation tasks. However, in traditional methods multiple concepts are usually sequentially annotated, i.e., each concept is exhaustively annotated before proceeding to the next, without taking the learnabilities of different concepts into consideration. Furthermore, in most of these methods only a single modality is applied. This paper presents a novel multi- concept multi-modality active learning method which ex- changeably annotates multiple concepts in the context of multi-modality. It iteratively selects a concept and a batch of unlabeled samples, and then these samples are annotated with the selected concept. Afier that, a graph-based semi-supervised learning is conducted on each modality for the selected concept. The proposed method takes into account both the learnabilities of different concepts and the potentials of different modalities. Experimental results on TRECVID 2005 benchmark have demonstrated its effectiveness and efficiency.
Starting Page	321
Ending Page	328
File Size	680002
Page Count	8
File Format	PDF
ISBN	9780769529974
DOI	10.1109/ICSC.2007.14
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning systems Asia Humans Video compression Semisupervised learning Large-scale systems Labeling Videoconference
Content Type	Text
Resource Type	Article

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