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

Published

November 5, 2013

Author(s)

Afzal A. Godil, Asim Wagan

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 Title

ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM BCB), 20013

Conference Dates

September 22-25, 2013

Conference Location

Washington, DC, DC

Pub Type

Conferences

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Keywords

Bioimage, classification, local features, bag of visual words

Citation

Godil, A. and Wagan, A. (2013), Exploring Local Features and the Bag-of-Visual-Words Approach for BioImage Classification, ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM BCB), 20013, Washington, DC, DC, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=914571 (Accessed September 20, 2024)