A Comparative Study of Data Mining approaches for Bag of Visual Words Based Image Classification
No. 1 (2018), Articles
No. 1 (2018)

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

Articles
Published 2021-05-16
  • Sahar Abd El Hameed Mokhtar
Sahar Abd El Hameed Mokhtar
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Keywords

Image Classification, Data Mining, Machine Learning, Neural Networks, Decision Trees, Bayesian Network, Discriminant Analysis, K Nearest Neighbor

Abstract

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.

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