Affect-Based Retrieval of Landscape Images Using Probabilistic Affective Model

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• 作者：Yunhee Shin (17)
  Eun Yi Kim (18)
  Tae-Eung Sung (19)
  
• 关键词：Affect ; based image retrieval ; probabilistic affective model ; meanshift clustering ; color image scale
• 刊名：Lecture Notes in Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：8008
• 期：1
• 全文大小：492KB
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  4. Shin, Y., Kim, E.Y., Kim, Y.: Automatic textile image annotation by prediction emotional concepts from visual features. Image and Vision Computing聽28, 526鈥?37 (2010) CrossRef
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• 作者单位：Yunhee Shin (17)
  Eun Yi Kim (18)
  Tae-Eung Sung (19)
  
  17. Department of Patent Valuation & Licensing, Korea Invention Promotion Association, Seoul, Korea
  18. Visual Information Processing Lab., Konkuk University, Seoul, Korea
  19. Department of Technology Commercialization Information, Korea Institute of Science and Technology Information, Seoul, Korea
  

文摘

We consider the problem of ranking the web image search using human affects. For this, a Probabilistic Affective Model (PAM) is presented for predicting the affects from color compositions (CCs) of images, then the retrieval system is developed using them. The PAM first segments an image into seed regions, then extracts CCs among seed regions and their neighbors, finally infer the numerical ratings of certain affects by comparing the extracted CCs with pre-defined human-devised color triplets. The performance of the proposed system has been studied at an online demonstration site where 52 users search 16,276 landscape images using affects, then the results demonstrated its effectiveness in affect-based image annotation and retrieval.