Abstract
Nowadays, bioimages such as microscopic images and in situ hybridization images increase exponentially. The rapid growth of such images calls for efficient and effective methods for mining significant patterns in them. As a biological process usually consists of several temporal stages, one important task in bioimage analysis is to classify images into different stages. In this paper, a multi-layer model collaboration approach is proposed to capitalize the class correlations in order to enhance the multi-class classification accuracy. First, several middle-level classes, which are relatively easy to annotate are created. A set of subspace-based classifiers are trained. Next, the classification scores output from these models are integrated with the target class classification scores. The score integration problem was formulated as a convex optimization problem, which is solved by the gradient descent approach. Experiments on four biological image data sets demonstrate that the proposed framework outperforms other current state-of-the-art algorithms, which indicates the proposed framework is promising.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.