By Stéphane Marchand-Maillet, Eric Bruno
This booklet constitutes the completely refereed post-proceedings of the 4th foreign Workshop on Adaptive Multimedia Retrieval, AMR 2006, held in Geneva, Switzerland in July 2006.
The 18 revised complete papers awarded including 2 invited papers have been rigorously chosen in the course of rounds of reviewing and development. additionally incorporated are invited contributions which were meant to open on less-addressed issues locally, because it is the case for track info retrieval and disbursed details retrieval. The papers are prepared in topical sections on ontology-based retrieval and annotation, score and similarity measurements, tune details retrieval, visible modelling, adaptive retrieval, structuring multimedia, in addition to consumer integration and profiling.
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Extra resources for Adaptive Multimedia Retrieval:User, Context, and Feedback: 4th International Workshop, AMR 2006, Geneva, Switzerland, July, 27-28, 2006, Revised ... Applications, incl. Internet/Web, and HCI)
Statistical cues for domain speciﬁc image segmentation withperformance analysis. In IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages 125–132, 2000. 16. S. Kosinov and S. Marchand-Maillet. Overview of approaches to semantic augmentation of multimedia databases for eﬃcient access and content retrieval. In Adaptive Multimedia Retrieval, Postproc. of 1st Int. Workshop, pages 19–35, 2004. 17. J. Lu, S. ping Ma, and M. Zhang. Automatic image annotation based-on model space.
Workshop, pages 44–54. Springer-Verlag, 2006. 5. N. V. Boulgouris, I. Kompatsiaris, V. Mezaris, D. Simitopoulos, and M. G. Strintzis. Segmentation and content-based watermarking for color image and image region indexing and retrieval. In EURASIP Journal on Applied Signal Processing, pages 418–431. Hindawi Publishing Corporation, 2002. 26 C. Hentschel et al. 6. C. Carson, S. Belongie, H. Greenspan, and J. Malik. Blobworld: Image segmentation using expectation-maximization and its application to image querying.
Features characterize low-level visual content such as color, texture, shapes, and possibly other features. Objects and events describe semantic content. Feature-based models use automatically extracted features, which represent the content of a video, but they hardly provide semantics that describe high-level video concepts. Therefore, low-level features alone are not suﬃcient to fulﬁll the users need alone. Because it is very diﬃcult to explore semantic content from the raw video data, semantic models usually use free text/attribute/keywords annotation to represent high-level concepts of the video data which results in many drawbacks.