Histology Based Image Retrieval in Multifeature Spaces
D.Sudha1, J.Priyadarshini2, A.Ranjidha3
1D. Sudha, M.E Degree, Department of Computer Science, Anna University, Chennai (Tamil Nadu), India.
2Dr. J.Priyadarshini, B.E Degree, Department of Computer Science, Anna University, Chennai (Tamil Nadu), India.
3A. Ranjidha, M.E Degree, Department of Computer Science, Anna University, Chennai (Tamil Nadu), India.
Manuscript received on 13 February 2014 | Revised Manuscript received on 20 February 2014 | Manuscript Published on 28 February 2014 | PP: 59-62 | Volume-3 Issue-9, February 2014 | Retrieval Number: I1483023914/14©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Content-based histology image retrieval systems have shown great potential in supporting decision making in clinical activities, teaching, and biological research. In content-based im-age retrieval, feature combination plays a key role. It aims at en-hancing the descriptive power of visual features corresponding to semantically meaningful queries. It is particularly valuable in his-tology image analysis where intelligent mechanisms are needed for interpreting varying tissue composition and architecture into histological concepts. This paper presents an approach to auto-matically combine heterogeneous visual features for histology im-age retrieval. The aim is to obtain the most representative fusion model for a particular keyword that is associated with multiple query images. The core of this approach is a multiobjective learn-ing method, which aims to understand an optimal visual-semantic matching function by jointly considering the different preferences of the group of query images. The task is posed as an optimization problem, and a multiobjective optimization strategy is employed in order to handle potential contradictions in the query images associated with the same keyword. Experiments were performed on two different collections of histology images. The results show that it is possible to improve a system for content-based histology image retrieval by using an appropriately defined multifeature fu-sion model, which takes careful consideration of the structure and distribution of visual features.
Keywords: Content-Based Image Retrieval (CBIR), Feature Fusion, Histology Image Retrieval, Multiobjective Optimization.
Scope of the Article: Image Security