Logo Classification with Edge-Based DAISY Descriptor

Abstract

For the classification of logo images, there are significant challenges in the classification of merchandise logos such that only a few key points can be found in the relatively small logo images due to large variations in texture, poor illumination and generally, lack of discriminative features. This paper addresses these difficulties by introducing an integrated approach to classify merchandise logos with the combination of local edge-based descriptor-DAISY, spatial histogram and salient region detection. During the training phase, after carrying out the edge extraction, merchandise logos are described with a set of SIFT-like DAISY descriptors which is computed efficiently and densely along edge pixels. Visual word vocabulary generation and spatial histogram are used for describing the images/regions. Saliency map for object detection is adopted to narrow down and localize the logos. The feature map for approximating a non-linear kernel is also used to facilitate the classification by a linear SVM classifier. The experimental results demonstrate that the Edge-based DAISY (EDAISY) descriptor outperforms the state-of-the-art SIFT and DSIFT descriptors in terms of classification accuracy on a set of collected logo image dataset.