Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
3D Part identification based on local shape descriptors
PerMIS '08 Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems
Hi-index | 0.00 |
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 dataset of 2D HeLa from CMU and have achieved performance similar to the state of the art.