A Comparative Study of Data Mining approaches for Bag of Visual Words Based Image Classification

Image classification is one of the most significant and challenging tasks in computer vision. The goal of this task is to build a system that is capable to reveal an image label within a collection of different image categories. This paper presents and discusses the application of various data mining techniques for image classification based on Bag of Visual Words (BoVW) feature extraction algorithm. The BoVW model is constructed using grey level features: The Speeded Up Robust Features (SURF) and Maximally Stable Extremal Regions (MSER) descriptors along with color features: Color correlograms and Improved Color Coherence Vector (ICCV). Five data mining techniques; Neural Networks (NN), Decision Trees (DT), Bayesian Network (BN), Discriminant Analysis (DA) and K Nearest Neighbor (KNN), are explored and evaluated on two large different datasets: Corel-1000 and COIL-100. The experimental results illustrate that BN and DA outperform the other data mining methods considered in this comparative study. For Corel-1000 dataset, BN and DA achieved an average accuracy and specificity of about 99.9% and an average sensitivity of about 99.5 and 99.4%, respectively. While for the COIL-100 dataset, BN and DA accomplished an average accuracy and sensitivity of about 100% and an average specificity of about 98.5 and 98.9, respectively.