Download e-book for iPad: Computer Vision -- ACCV 2014: 12th Asian Conference on by Daniel Cremers, Ian Reid, Hideo Saito, Ming-Hsuan Yang

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By Daniel Cremers, Ian Reid, Hideo Saito, Ming-Hsuan Yang

ISBN-10: 3319168649

ISBN-13: 9783319168647

ISBN-10: 3319168657

ISBN-13: 9783319168654

The five-volume set LNCS 9003--9007 constitutes the completely refereed post-conference lawsuits of the twelfth Asian convention on computing device imaginative and prescient, ACCV 2014, held in Singapore, Singapore, in November 2014.

The overall of 227 contributions awarded in those volumes was once conscientiously reviewed and chosen from 814 submissions. The papers are equipped in topical sections on attractiveness; 3D imaginative and prescient; low-level imaginative and prescient and contours; segmentation; face and gesture, monitoring; stereo, physics, video and occasions; and poster periods 1-3.

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Additional info for Computer Vision -- ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part I

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171–182. Springer, Heidelberg (2015) 7. : Tell me what you like and i’ll tell you what you are: discriminating visual preferences on flickr data. , Hu, Z. ) ACCV 2012, Part I. LNCS, vol. 7724, pp. 45–56. Springer, Heidelberg (2013) 8. : Learning to predict where humans look. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2106–2113. IEEE (2009) 9. : 2d articulated human pose estimation and retrieval in (almost) unconstrained still images. Int. J. Comput. Vis. 99, 190–214 (2012) 10.

In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 761–770. ACM (2005) 22. : Learning sparse SVM for feature selection on very high dimensional datasets. In: ICML, pp. 1047–1054 (2010) 23. : Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th Annual Conference on Computational Learning Theory, pp. 92–100 (1998) 24. : A co-regularized approach to semi-supervised learning with multiple views. In: Proceedings of the ICML Workshop on Learning with Multiple Views (2005) 25.

Our goal is to select a subset of retrieved images S ⊆ V. We first define the relative ranking between a pair of images and then use it to measure the “inter-rank” consensus amongst multiple ranked lists. , rm,K ) , where rm,i is the position of image Ii in the m-th ranked list. Smaller value means higher rank. The relative 26 F. Yang et al. Fig. 2. The effectiveness of the relative ranking consistency measure. The set V contains K = 100 images, from which we need to select an image into S, which currently contains two images.

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Computer Vision -- ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part I by Daniel Cremers, Ian Reid, Hideo Saito, Ming-Hsuan Yang


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