By Arnold W. M. Smeulders (auth.), Nicu Sebe, Yuncai Liu, Yueting Zhuang, Thomas S. Huang (eds.)
Welcome to the overseas Workshop on Multimedia content material research and Mining, MCAM 2007. Our workshop offers a picture of the present wor- broad study in multimedia research. via contemporary advances in computing, networking, and knowledge garage, multimedia will create new attention-grabbing technical percentages in quite a lot of ?elds, resembling leisure, trade, technology, drugs, and public security. To bene?t from this strength, builders desire - responsible strategies for the research, seek, and administration of multimedia info, in addition to disbursed method architectures within which those thoughts might be embedded to e?ectively support the clients. lately, there were many workshops and conferences devoted to M- timedia, yet so much of them the normal trend consisting commonly of oral and poster shows. all of us believe that there's a desire for a gathering that truly is a workshop, i.e., one who presents loads of possibilities for discussions and dissemination. therefore, the structure of the workshop comprises four p- els on scorching issues in multimedia followedby discussions and poster displays. the themes of the panels are: Multimedia research and purposes, Multi- dia seek and Mining, P2P Streaming, and protection. This quantity incorporates a variety of invited contributions from specialists within the quarter in addition to the chosen normal contributions. This 12 months 139 submissions from thirteen international locations have been submitted and forty six have been accredited for presentation on the convention after being reviewed via this system Committee members.
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Extra info for Multimedia Content Analysis and Mining: International Workshop, MCAM 2007, Weihai, China, June 30-July 1, 2007. Proceedings
Maintaining sequentiality while inserting new objects would have to be done either by leaving empty slots in the list while creation, or by recreating the lists when new objects are inserted. 4 Empirical Study In this section, we brieﬂy present experimental results using PMF. To study the eﬀectiveness of PMF, we used an image replica dataset based on the Corel image database The image replica dataset contains 2000 images divided equally into 50 classes. Each class consists of an image from the Corel image database and 39 near-replicas of the image, which were created as in .
References 1. : Enhanced perceptual distance functions and indexing for image replica recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(3), 379–391 (2005) 2. : Similarity searching: a matching based approach. In: VLDB (2006) 3. : Similarity, interactive activation, and mapping. Journal of Experimental Psychology: Learning, Memory, and Cognition 20, 3–28 (1994) 32 A. Y. Chang 4. : Dyndex: a dynamic and non-metric space indexer. In: ACM International Conference on Multimedia (2002) 5.
Li et al. images ,. Intuitively, if a well annotated and unlimited-scale image database is available, then for any query image, we can find its near-duplicate in this database and simply propagate its annotation to the query image. In a more realistic case that the image database is of limited scale, we can still find a group of very similar images in terms of either global features or local features, extract salient phrases from their descriptions, and select the most salient ones to annotate the query image.