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Notice of Violation of IEEE Publication Principles
"CTMIR: A Novel Correlated Topic Model for Image Retrieval"
by Jian Wen Tao and Pei Fen Ding
in the Proceedings of the Second International Workshop on Knowledge Discovery and Data Mining, WKDD 2009 pp.948-951, Jan. 2009
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.
This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:
"Correlated Topic Models for Image Retrieval"
by T. Greif, E. Horster, R. Lienhart
in Report 2008-09, Institut fur Informatik, Universitat Augsberg, July 2008
Representation of images by the Latent Dirichlet Allocation model combined with an appropriate similarity measure is suitable for performing large scale image retrieval in a real-world database. The LDA model, however, relies on the assumption that all topics are independent of each other something that is obviously not true in most cases. In this work we study a recently proposed model, the Correlated Topic Model (CTM) , in the context of large-scale image retrieval. This approach is able to explicitly model such correlations of topics. We experimentally evaluate the proposed retrieval approach on a real-world large-scale database consisting of more than 246,000 images and compare the performance to related approaches.