Bag-of-features based medical image retrieval via multiple assignment and visual words weighting
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Computational Bioscience Research Center (CBRC)
Structural and Functional Bioinformatics Group
MetadataShow full item record
AbstractBag-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. © 2011 IEEE.
SponsorsThe work was supported by the Major State Basic Research Development Program of China (973 Program) under Grant 2010CB834303 and a grant from King Abdullah University of Science and Technology.
- A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval.
- Authors: Yang L, Jin R, Mummert L, Sukthankar R, Goode A, Zheng B, Hoi SC, Satyanarayanan M
- Issue date: 2010 Jan
- Learning semantic and visual similarity for endomicroscopy video retrieval.
- Authors: Andre B, Vercauteren T, Buchner AM, Wallace MB, Ayache N
- Issue date: 2012 Jun
- A thousand words in a scene.
- Authors: Quelhas P, Monay F, Odobez JM, Gatica-Perez D, Tuytelaars T
- Issue date: 2007 Sep
- Visual pattern mining in histology image collections using bag of features.
- Authors: Cruz-Roa A, Caicedo JC, González FA
- Issue date: 2011 Jun
- A unified framework for image retrieval using keyword and visual features.
- Authors: Jing F, Li M, Zhang HJ, Zhang B
- Issue date: 2005 Jul