By Guangyu Zhu, Shuicheng Yan, Tony X. Han, Changsheng Xu (auth.), Kuo-Tien Lee, Wen-Hsiang Tsai, Hong-Yuan Mark Liao, Tsuhan Chen, Jun-Wei Hsieh, Chien-Cheng Tseng (eds.)
This two-volume lawsuits constitutes the refereed papers of the seventeenth overseas Multimedia Modeling convention, MMM 2011, held in Taipei, Taiwan, in January 2011. The fifty one revised usual papers, 25 specified consultation papers, 21 poster consultation papers, and three demo consultation papers, have been rigorously reviewed and chosen from 450 submissions. The papers are geared up in topical sections on audio, photo video processing, coding and compression; media content material searching and retrieval; multi-camera, multi-view, and 3D structures; multimedia indexing and mining; multimedia content material research; multimedia sign processing and communications; and multimedia functions. The targeted consultation papers take care of content material research for human-centered multimedia purposes; huge scale wealthy media facts administration; multimedia figuring out for buyer electronics; photo item popularity and compression; and interactive picture and video search.
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Additional info for Advances in Multimedia Modeling: 17th International Multimedia Modeling Conference, MMM 2011, Taipei, Taiwan, January 5-7, 2011, Proceedings, Part II
MMM 2011, Part II, LNCS 6524, pp. 12–24, 2011. c Springer-Verlag Berlin Heidelberg 2011 Grid-Based Retargeting with Transformation Consistency Smoothing 13 Fig. 1. Illustrating image/video retargeting from a supervised learning viewpoint Many content-aware retargeting methods have been proposed such as cropping[4,5,1], seam craving[13,9,15,11], multi-operator, mesh based retargeting[12,18,3,7,17]. Cropping[4,5,1] works on an attention model to detect important regions, and then crops the most important region to display.
In the subsequent user study, most subjects prefer (f) since its entire consistency are best achieved, even though the grids covering the girl are somehow squeezed. Furthermore, our method are compared with the scaling and other two representative methods [18,9]. Note that when noisy importance maps in source images (especially for structure and close up) cannot delineate important objects from non-important regions, the retargeting problem become more challenging. g. the sixth row in Fig 5). In contrast, the seam carving method  brings about considerable shape artifacts of an object, especially more structured ones, as indicated in the 1, 2, 3, 4, 6, 7 rows in Fig 5), since, at object regions, some seams Grid-Based Retargeting with Transformation Consistency Smoothing (a) (b) (c) (d) (e) 21 (f) Fig.
Fig. 3 shows the recognition results by using diﬀerent types of interaction features as well as their combination compared with the state-of-the-art performance in . In , the SIFT, HoF and HoG descriptors are extracted from the spatio-temporal salient points detected by 2D and 3D Harris detectors. Bag-of-words features are built as the compact representation for the video activities, which are the input of the SVM classiﬁer. From Fig. 3, we can conclude that our quaternion descriptor and generative model yields the highest AP performance than the latest report.
Advances in Multimedia Modeling: 17th International Multimedia Modeling Conference, MMM 2011, Taipei, Taiwan, January 5-7, 2011, Proceedings, Part II by Guangyu Zhu, Shuicheng Yan, Tony X. Han, Changsheng Xu (auth.), Kuo-Tien Lee, Wen-Hsiang Tsai, Hong-Yuan Mark Liao, Tsuhan Chen, Jun-Wei Hsieh, Chien-Cheng Tseng (eds.)