Abstract
In the computer vision research field, the “Bag of Words” model is known as a popular method for image representation. The Information Bottleneck principle derived from the rate-distortion theory in basic information theory has been applied to many applications in machine learning. In this paper, we introduce a method which combines the two state-of-the-art techniques for image clustering. Images are firstly represented using the “Bag of Words” model, and in the process of clustering based on Information Bottleneck principle, we utilize the Bregman divergence algorithm which works like k-means to get the optimal clustering result. Through the experimental results, we present several points of improvement obtained by the proposed method.
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Bai, X., Luo, S. (2010). Combining Bag of Words Model and Information Theoretic Method for Image Clustering. In: Zeng, Z., Wang, J. (eds) Advances in Neural Network Research and Applications. Lecture Notes in Electrical Engineering, vol 67. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12990-2_48
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DOI: https://doi.org/10.1007/978-3-642-12990-2_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12989-6
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