The Dual Codebook: Combining Bags of Visual Words in Image Classification
Jonathan L. Maas, Emmanuel Okafor, Marco Wiering · University of Groningen research database (University of Groningen / Centre for Information Technology) · 2016
In this paper, we evaluate the performance of two conventional bag of words approaches, using two basic local feature descriptors, to perform image classification. These approaches are compared to a novel design which combines two bags of visual words, using two different feature descriptors. The system extends earlier work wherein a bag of visual words approach with an L2 support vector machine classifier outperforms several alternatives. The descriptors we test are raw pixel intensities and the Histogram of Oriented Gradients. Using a novel Primal Support Vector Machine as a classifier, we perform image classification on the CIFAR-10 and MNIST datasets. Results show that the dual codebook implementation successfully utilizes the potential contributive information encapsulated by an alternative feature descriptor and increases performance, improving classification by 5-18% on CIFAR-10, and 0.22-1.03% for MNIST compared to the simple bag of words approaches.