By Daniel Cremers, Ian Reid, Hideo Saito, Ming-Hsuan Yang
The five-volume set LNCS 9003--9007 constitutes the completely refereed post-conference complaints of the twelfth Asian convention on desktop imaginative and prescient, ACCV 2014, held in Singapore, Singapore, in November 2014.
The overall of 227 contributions offered in those volumes was once rigorously reviewed and chosen from 814 submissions. The papers are prepared in topical sections on reputation; 3D imaginative and prescient; low-level imaginative and prescient and contours; segmentation; face and gesture, monitoring; stereo, physics, video and occasions; and poster classes 1-3.
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Extra resources for Computer Vision -- ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part I
Problem. t. S ⊆ V, |S| ≤ Ks (7) where λ is a pre-deﬁned weighting factor balancing the two terms. Ks is the largest number of selected images, which means we only select and rerank at most Ks images. Equation 7 is submodular and non-decreasing since it is a linear combination of submodular and non-decreasing functions. Direct optimization of Eq. 7 is a NP-hard problem, but it can be approximately optimized by a greedy algorithm. Starting from an empty set S = ∅, the greedy algorithm iteratively adds a new element to S which provides the largest marginal gain at each iteration, until Ks elements have been selected.
Cremers et al. ): ACCV 2014, Part I, LNCS 9003, pp. 19–34, 2015. 1007/978-3-319-16865-4 2 20 F. Yang et al. query-speciﬁc graph fusion  or Co-Regularized Multi-Graph Learning . In  requires a large number of queries to compute relevance scores for initially retrieved images, which is only suitable for large sets of queries. In , initial ranked lists were converted to undirected graphs, which were linearly combined without considering the inter-relationships between modalities. In  is a supervised learning based on image attributes, so it is not suitable for unsupervised reranking tasks.
2d articulated human pose estimation and retrieval in (almost) unconstrained still images. Int. J. Comput. Vis. 99, 190–214 (2012) 10. : Detecting people looking at each other in videos. Int. J. Comput. Vis. 106, 282–296 (2014) 11. : Face detection, pose estimation, and landmark localization in the wild. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2879–2886 (2012) 12. ”: appreciating the human body on social network sites. , Øhrstrøm, P. ) PERSUASIVE 2008. LNCS, vol.
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