Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Video information retrieval using objects and ostensive relevance feedback
Proceedings of the 2004 ACM symposium on Applied computing
Efficient contour-based shape representation and matching
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
A usage study of retrieval modalities for video shot retrieval
Information Processing and Management: an International Journal
Object-based interactive video access for consumer-driven advertising
EC-Web'07 Proceedings of the 8th international conference on E-commerce and web technologies
Progress in information retrieval
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
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Recent years have seen the development of different modalities for video retrieval. The most common of these are (1) to use text from speech recognition or closed captions, (2) to match keyframes using image retrieval techniques like colour and texture [6] and (3) to use semantic features like “indoor”, “outdoor” or “persons”. Of these, text-based retrieval is the most mature and useful, while image-based retrieval using low-level image features usually depends on matching keyframes rather than whole-shots. Automatic detection of video concepts is receiving much attention and as progress is made in this area we will see consequent impact on the quality of video retrieval. In practice it is the combination of these techniques which realises the most useful, and effective, video retrieval as shown by us repeatedly in TRECVid [5].