Advances in Multimedia Information Processing – PCM 2007: by Yijuan Lu, Jingsheng Ma, Qi Tian (auth.), Horace H.-S. Ip,

By Yijuan Lu, Jingsheng Ma, Qi Tian (auth.), Horace H.-S. Ip, Oscar C. Au, Howard Leung, Ming-Ting Sun, Wei-Ying Ma, Shi-Min Hu (eds.)

The Pacific-Rim convention on Multimedia (PCM) was once held in Hong Kong, on the urban collage of Hong Kong, in the course of December 11–14, 2007. all started in 2000, PCM has been held in numerous areas round the Pacific Rim, together with Sydney, Beijing, Hsinchu, Singapore, Tokyo, Jeju, and Zhejiang in chronological order. PCM is a huge annual overseas convention prepared as a discussion board for the dissemination of state of the art technological advances and learn ends up in the fields of theoretical, experimental, and utilized multimedia research and processing. PCM 2007 used to be prepared into five various tracks with a complete of 247 submissions from 26 nations and areas together with Australia, Belgium, Canada, China, Japan, Korea, New Zealand, Singapore, Spain, Taiwan, the united kingdom and the united states. one of the 5 tracks, “multimedia research and retrieval” got the main variety of submissions (34% of the submissions). After a rigorous evaluate technique, seventy three papers have been approved for oral displays, giving an attractiveness fee of less than 29% for PCM 2007. moreover, 21 papers have been authorized for poster displays. we want to thank the entire song Chairs and the reviewers for his or her well timed dealing with of the paper experiences. we're rather thankful to Chong-Wah Ngo and his group for his or her help of the Web-based evaluation method in the course of the method. we're additionally indebted to the specific classes Chairs, Qi Tian and Timothy Shih, for the association of the 2 targeted periods on “The AVS China nationwide common” and “Multimedia info approach for Biomedical Research,” respectively.

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S. -H. Lim due to software editing. Removing these types of photos will definitely yield a better classification accuracy. However, we chose not to do so as manual inspection will be required in order to filter out these photos. In addition, as the testing photos are taken on manual settings, it is expected that the users may have different settings from one another even though it is for the same type of shot. This is particularly so due to the diverse intent and skill of the photographers. This presents another challenge that we need to work on in order to factor this diversity in our methodology.

Suppose the user uses the image (a) in Figure 1 as a query image, which has been reasonably well-segmented, what is his/her intention? Is the user looking for images with a cow, or grassland, or lake, or all of them? Even another human user can not give the answer without other priors. g. (d) and (e) in Figure 1, as positive feedback, it is very reasonable to assume that the user is actually interested in images with cows. g. [8, 9, 17] combine image segmentation and relevance feedback and obtain good results.

CVPR 2006 (2006) 12. : Optimizing Learning in Image Retrieval. In: Proc. CVPR 2000 (2000) 13. : Relevance feedback: A power tool in interactive content-based image retrieval. IEEE Trans. on Circuits and Systems for Video Technology 8(5), 644–655 (1998) 14. : Normalized cuts and image segmentation. In: Proc. CVPR 1997(1997) 15. : Support vector machine active learning for image retrieval. In: Proc. ACM International Multimedia Conference (2001) 16. : Learning from user feedback in image retrieval system.

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