Clustering analysis and semantics annotation of 3d models based on users' implicit feedbacks

Authors:
Lv Tianyang;Huang Shaobin;Wu Peng;Lang Dapeng
Affiliations:
College of Computer Science and Technology, Harbin Engineering University, Harbin, China,National Audit Office, Audit Research Institute, Beijing, China,National Audit Office, Computer Technology ...;College of Computer Science and Technology, Harbin Engineering University, Harbin, China;College of Computer Science and Technology, Harbin Engineering University, Harbin, China;College of Computer Science and Technology, Harbin Engineering University, Harbin, China
Venue:
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Year:
2013

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Abstract

The performance of 3D model retrieval can be greatly improved by adopting precise semantics. Since manual annotation of semantics is too time-consuming, it is necessary to explore an automatic or semi-automatic way. Although it is widely accepted that users' feedbacks contain semantics, previous researches usually utilize relevance feedbacks in computing similarity of 3D models. The paper proposes a strategy for semantics clustering, annotation and retrieval of 3D models, which adopts not only relevance feedbacks but also noisy user operations. The strategy first converts implicit feedbacks into a weighted semantics network of 3D models. After analyzing this semantics network, this paper proposes an agglomerative hierarchical clustering method based on a novel concept of semantics core to obtain the semantics communities under different granularity. Finally, this paper shows an automatic semantics annotation method using the semantics of only a few 3D models. The proposed method is verified by simulated feedbacks with strong noise and real feedbacks of the Princeton Shape Benchmark. Our experiments show that the strategy achieve good performance not only in semantics clustering but also in semantics annotation.