Using Low Level Gradient Channels for Computationally Efficient Object Detection and Its Application in Logo Detection

Abstract

We propose a logo detection approach which utilizes the Haar (Haar-like) features computed directly from the gradient orientation, gradient magnitude channels and the gray intensity channel to effectively and efficiently extract discriminating features for a variety of logo images. The major contributions of this work are two-fold: 1) we explicitly demonstrate that, with an optimized design and implementation, the considerable discrimination can be obtained from the simple features like the Haar features which are extracted directly from the low level gradient orientation and magnitude channels, 2) we proposed an effective and efficient logo detection approach by using the Haar features obtained directly from gradient orientation, magnitude, and gray image channels. The experimental results on the collected merchandise images of Louis Vuitton (LV) and Polo Ralph Lauren (PRL) products show promising applicabilities of our approach.