Performance Analysis of Classifiers with Feature Selection and Optimization in CBIR System for Biological Images

The core objective of this paper is to improve the performance of Content Based Image Retrieval (CBIR) system for biological images by intelligent selection of discriminative set of feature vectors from the canonical set of feature vectors. The performance of the CBIR system can be further enhanced by proper selection of a classifier and fine tuning of model parameters to obtain improved classification accuracy. We extracted canonical set of feature vectors from biological images using a popular tool called Weighted Neighbor Distance using Compound Hierarchy of algorithms Representing Morphology (Wndchrm). We adopted a two step approach for the selection of features. The first step is to partition the canonical set of feature vectors into four distinct feature vector sets based on the methodology and algorithms for extraction of features. The second step is to perform Principal Component Analysis (PCA) and Fisher Score based selection of features from the partitioned set of feature vectors. The optimum set of feature vectors thus obtained is applied as training patterns to different classifier implementations such as Bayesian and Support Vector Machine (SVM) Classifiers. We used IICBU-2008 benchmark dataset [5, 7] of biological images for our experiments. We also compared the results with the results available for the wndchrm classifier. The results show that careful selection of the features and optimization of the classifier performance will lead to efficient implementation of CBIR systems.