Notice of Violation of IEEE Publication Principles: Bag-of-Features Based Medical Image Retrieval via Multiple Assignment and Visual Words Weighting

Notice of Violation of IEEE Publication Principles

"Bag-of-Features Based Medical Image Retrieval via Multiple Assignment and Visual Words Weighting"
by Jingyan Wang, Yongping Li, Ying Zhang, Chao Wang, Honglan Xie, Guoling Chen, and Xin Gao
in the IEEE Transactions on Medical Imaging, Vol. 30, No. 11, November 2011, pp. 1996-2011

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 substantial duplication 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:

"Histopathy Image Classification Using Bag of Features and Kernel Functions"
by Juan C. Caicedo, Angel Cruz, and Fabio Gonzalez
in Lecture Notes in Computer Science. Artificial Intelligence in Medicine, AIME-09, July 2009.Volume 5651/2009, pp 126-135

Bag-of-features based approaches have become prominent for image retrieval and image classification tasks in the past decade. Such methods represent an image as a collection of local features, such as image patches and key points with scale invariant feature transform (SIFT) descriptors. To improve the bag-of-features methods, we first model the assignments of local descriptors as contribution functions, and then propose a novel multiple assignment strategy. Assuming the local features can be reconstructed by their neighboring visual words in a vocabulary, reconstruction weights can be solved by quadratic programming. The weights are then used to build contribution functions, resulting in a novel assignment method, called quadratic programming (QP) assignment. We further propose a novel visual word weighting method. The discriminative power of each visual word is analyzed by the sub-similarity function in the bin that corresponds to the visual word. Each sub-similarity function is then treated as a weak classifier. A strong classifier is learned by boosting methods that combine those weak classifiers. The weighting factors of the visual words are learned accordingly. We evaluate the proposed methods on medical image retrieval tasks. The methods are tested on three well-known data sets, i.e., the ImageCLEFmed data set, the 304 CT Set, and the basal-cell carcinoma image set. Experimental results demonstrate that the proposed QP assignment outperforms the traditional nearest neighbor assignment, the multiple assignment, and the soft assignment, whereas the proposed boosting based weighting strategy outperforms the state-of-the-art weighting methods, such as the term frequency weights and the term frequency-inverse document frequency weights.