@inproceedings{3c236c3a2f8945cbab21f0fd2591d522,
title = "HaarHOG: Improving the HOG descriptor for image classification",
abstract = "The Histograms of Oriented Gradients (HOG) descriptor represents shape information by storing the local gradients in an image. The Haar wavelet transform is a simple yet powerful technique that can separately enhance the horizontal and vertical local features in an image. In this paper, we enhance the HOG descriptor by subjecting the image to the Haar wavelet transform and then computing HOG from the result in a manner that enriches the shape information encoded in the descriptor. First, we define the novel HaarHOG descriptor for grayscale images and extend this idea for color images. Second, we compare the image recognition performance of the HaarHOG descriptor with the traditional HOG descriptor in four different color spaces and grayscale. Finally, we compare the image classification performance of the HaarHOG descriptor with some popular descriptors used by other researchers on four grand challenge datasets.",
keywords = "Haar wavelets, Haarhog descriptor, Histograms of oriented gradients descriptor, Object and scene image classification, Shape descriptor",
author = "Sugata Banerji and Atreyee Sinha and Chengjun Liu",
year = "2013",
doi = "10.1109/SMC.2013.729",
language = "English (US)",
isbn = "9780769551548",
series = "Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013",
pages = "4276--4281",
booktitle = "Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013",
note = "2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 ; Conference date: 13-10-2013 Through 16-10-2013",
}