Keiji Yanai
ABSTRACT
We propose a new method to select relevant images to the given keywords from images gathered from theWeb based on the Probabilistic Latent Semantic Analysis (PLSA) model which is a probabilistic latent topic model originally proposed for text document analysis. The experimental results shows that the results by the proposed method is almost equivalent to or outperforms the results by existing methods. In addition, it is proved that our method can select more various images compared to the existing SVM-based methods.
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Digital Library
- G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91--110, 2004. Google ScholarDigital Library
- Monay and D. Gatica-Perez. Modeling semantic aspects for cross-media image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(10):1802--1817, 2007. Google ScholarDigital Library
- Yanai. Generic image classification using visual knowledge on the web. In Proc. of ACM International Conference Multimedia, pages 67--76, 2003. Google ScholarDigital Library
- Yanai. Image collector III: A web image-gathering system with bag-of-keypoints. In Proc. of the International World Wide Web Conference, 2007. Google ScholarDigital Library
- Yanai and K. Barnard. Probabilistic Web image gathering. In Proc. of ACM SIGMM International Workshop on Multimedia Information Retrieval, pages 57--64, 2005. Google ScholarDigital Library
Index Terms
- Automatic web image selection with a probabilistic latent topic model
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