Boosting scene understanding by hierarchical pachinko allocation

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• 作者：Jihong Ouyang ; Ximing Li ; Hongtu Li
• 关键词：Scene understanding ; VSIM ; hPAM ; Topic modeling
• 刊名：Multimedia Tools and Applications
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：75
• 期：20
• 页码：12581-12595
• 全文大小：1,309 KB
• 刊物类别：Computer Science
• 刊物主题：Multimedia Information Systems
  Computer Communication Networks
  Data Structures, Cryptology and Information Theory
  Special Purpose and Application-Based Systems
  
• 出版者：Springer Netherlands
• ISSN：1573-7721
• 卷排序：75

文摘

Scene understanding is a popular research direction. In this area, many attempts focus on the problem of naming objects in the complex natural scene, and visual semantic integration model (VSIM) is the representative. This model consists of two parts: semantic level and visual level. In the first level, it uses a four-level pachinko allocation model (PAM) to capture the semantics behind images. However, this four-level PAM is inflexible and lacks of considerations of common subtopics that represent the background semantics. To address these problems, we use hierarchical PAM (hPAM) to replace PAM. Since hPAM is flexible, we investigate two variations of hPAM to boost VSIM in this paper. We derive the Gibbs sampler to learn the proposed models. Empirical results validate that our works can obtain better performance than the state-of-the-art algorithms.