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

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 computing device imaginative and prescient, ACCV 2014, held in Singapore, Singapore, in November 2014.

The overall of 227 contributions offered in those volumes used to be conscientiously reviewed and chosen from 814 submissions. The papers are geared up in topical sections on popularity; 3D imaginative and prescient; low-level imaginative and prescient and lines; segmentation; face and gesture, monitoring; stereo, physics, video and occasions; and poster classes 1-3.

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Read or Download Computer Vision -- ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part I PDF

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Additional resources 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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7. Features weighted for three users and a sample of images they tagged as favorite. Users’ preferences can be represented using high-level visual features. images for each user, which is similar to that used by [5,7]. And we test the method used in [7] on our dataset and use that as the baseline for comparing the performance of our method. The results (in Fig. , using visual features alone, using tag features alone, using bimodal features). The following observations can be made based on the Fig.

Query-specific graph fusion [10] or Co-Regularized Multi-Graph Learning [11]. In [9] requires a large number of queries to compute relevance scores for initially retrieved images, which is only suitable for large sets of queries. In [10], initial ranked lists were converted to undirected graphs, which were linearly combined without considering the inter-relationships between modalities. In [11] is a supervised learning based on image attributes, so it is not suitable for unsupervised reranking tasks.

Then we have the following proposition with the proof in the supplementary material. Proposition 2. T : 2V → R is a submodular and monotone function if elements in S are ordered with respect to a phantom item vp ∈ S and rvp = 0. 4 Optimization Combining the information gain and relative ranking consistency terms, we obtain the final objective function Q(S) = R(S) + λT (S) for the reranking 28 F. Yang et al. problem. t. S ⊆ V, |S| ≤ Ks (7) where λ is a pre-defined weighting factor balancing the two terms.

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