NIST logo

Publication Citation: Exploring Local Features and the Bag-of-Visual-Words Approach for BioImage Classification

NIST Authors in Bold

Author(s): Afzal A. Godil; Asim Wagan;
Title: Exploring Local Features and the Bag-of-Visual-Words Approach for BioImage Classification
Published: November 05, 2013
Abstract: With recent advances in imaging technologies large numbers of bioimages are currently being acquired. Automated classification of these bio-images is a very important and challenging problem. Here we investigate the capabilities of local features and the Bag-of-Visual-Words (BOV) approach in the area of bioimage classification. We have tested both sparse and dense placement of local features. The local feature that we have tested is Scale-Invariant Feature Transform (SIFT), but we are in the process of testing other local features. The standard BOV approach is based on counting the number of local descriptors assigned to each quantization. In our case we are also using other statistics (mean and covariance of local descriptors). The classifier used for this study is the Support Vector Machine (SVM). We have performed classification experimentation on the well-tested single cell data- set of 2D HeLa from CMU and have achieved performance similar to the state of the art.
Proceedings: ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM BCB), 20013
Pages: pp. 694 - 695
Location: Washington, DC, DC
Dates: September 22-25, 2013
Keywords: Bioimage, classification, local features, bag of visual words
Research Areas: Software, Information Processing Systems, Imaging
PDF version: PDF Document Click here to retrieve PDF version of paper (449KB)