Comparing HMAX and BoVW Models for Large-Scale Image Classification

• Main Authors: Filali, Jalila, Zghal, Hajer Baazaoui, Martinet, Jean
• Format: Conference Proceeding
• Language: English
• Subjects: Bag of Visual Words model; feature extraction; HMAX model; Image classification
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2021.08.117

Image classification is one of the most important topics in computer vision. It became crucial for large image datasets. In the literature, several image classification approaches are proposed. In this context, Bag-of-Visual Words (BoVW) model has been widely used. The BoVW model relies on building visual vocabulary and images are represented as histograms of visual words. However, recently, attention has been shifted to the use of complex architectures which are characterized by multilevel processing. HMAX (Hierarchical Max-pooling model) model has attracted a great deal of attention in image classification, due to its architecture, which alternates layers of feature extraction with layers of pooling. This paper aims at comparing bags of visual words model to HMAX model for image classification using large datasets. To achieve this goal, we study the use of image features obtained by BoVW model with SIFT (Scale-Invariant Feature Transform) descriptors, and we compare them to HMAX features. Image classification is performed by using the support vector machine (SVM) classifiers. Both HMAX and BoVW models are tested on ImageNet and OpenImages datasets and results have shown that the classification performance obtained by HMAX model outperforms the classification using BoVW model.