Probabilistic Latent Semantic Analysis for Sketch-based 3D Model Retrieval
文献类型：会议论文
| 作者 | Yafei Wen; Changqing Zou; Jianzhuang Liu |
| 出版日期 | 2014 |
| 会议名称 | 2014 4th IEEE International Conference on Information Science and Technology (ICIST) |
| 会议地点 | 中国 |
| 英文摘要 | Sketch-based 3D model retrieval has recently attracted a lot of interest in the content-based 3D model retrieval community. We propose a novel method to solve this problem by using a latent topic model probabilistic Latent Semantic Analysis (PLSA). PLSA is an unsupervised learning method used to learn the latent topics from text documents. Although pLSA is first proposed in the statistical text analysis and retrieval field, it has been demonstrated to be an effective topic model widely used in computer vision research. In this paper, a bag-of-features (BOF) model based on local features of sketches is employed to obtain the visual word vocabulary, and then the pLSA model is used to learn the latent semantic topic representations for sketches based on their visual word representations. A query sketch is matched with a 3D model within the latent semantic topic space to alleviate the semantic gap and decrease the matching time. We conduct our experiments based on the most popular local shape features in order to have a comprehensive study of this topic model for sketch-based 3D model retrieval. The experimental results on the common sketch-based watertight model benchmark show that our approach significantly outperforms the original word-occurrence statistic methods. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/5508]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2014 |
| 推荐引用方式 GB/T 7714 | Yafei Wen,Changqing Zou,Jianzhuang Liu. Probabilistic Latent Semantic Analysis for Sketch-based 3D Model Retrieval[C]. 见:2014 4th IEEE International Conference on Information Science and Technology (ICIST). 中国. |
入库方式： OAI收割
来源：深圳先进技术研究院
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