| Abstrakt: |
A critical issue in shape retrieval systems is that when a user submits a query shape, some shapes in the database are returned relatively often, while some are returned only when submitting specific queries. Intuitively, this phenomenon yields suboptimal retrieval accuracy. In this paper, we address the shape retrieval problem by casting it into the task of identifying “authority” nodes in an inferred similarity graph and also by re-ranking the shapes. The main idea is that the average similarity between a node and its neighboring nodes takes into account the local distribution, and therefore, helps modify the neighborhood edge weight, which guides the re-ranking. The proposed approach is evaluated on both 2D and 3D shape datasets, and the experimental results show that the proposed neighborhood induced similarity measure significantly improves the shape retrieval performance. Moreover, the computational speed of the proposed method is extremely fast.A critical issue in shape retrieval systems is that when a user submits a query shape, some shapes in the database are returned relatively often, while some are returned only when submitting specific queries. Intuitively, this phenomenon yields suboptimal retrieval accuracy. In this paper, we address the shape retrieval problem by casting it into the task of identifying “authority” nodes in an inferred similarity graph and also by re-ranking the shapes. The main idea is that the average similarity between a node and its neighboring nodes takes into account the local distribution, and therefore, helps modify the neighborhood edge weight, which guides the re-ranking. The proposed approach is evaluated on both 2D and 3D shape datasets, and the experimental results show that the proposed neighborhood induced similarity measure significantly improves the shape retrieval performance. Moreover, the computational speed of the proposed method is extremely fast. |
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