A Pyramid of Concentric Circular Regions to Improve Rotation Invariance in Bag-of-Words Approach for Object Categorization

Abstract

The bag-of-words (BoW) approach has shown to be effective in image categorization. Spatial pyramids in conjunction to the original BoW approach improve overall performance in the categorization process. This work proposes a new way of partitioning an image in concentric circular regions and calculating histograms of codewords for each circular region. The histogram of the entire image is concatenated forming the image descriptor. This slight and simple modification preserves the performance of the original spatial information and adds robustness to image rotation. The pyramid of concentric circular regions showed to be almost 78% more robust to rotation of images in our tests compared to the traditional rectangular spatial pyramids.