Logo Classification with Edge-Based DAISY Descriptor

Saliency-driven image classification method based on histogram mining and image score

“…To extract local features, one of the commonly used descriptors is the SIFT descriptor [1,16], which is a sparse descriptor. On the other hand, dense descriptors such as DSIFT, Phow, DAISY, and enhanced SIFT descriptor based on regular or random dense sampling have become the state-of-the-art and outperformed the sparse SIFT descriptor in the recent literature [11,[17][18][19]32]. On this note, a myriad of image classification works have integrated visual words from dense descriptor and achieved remarkable results [11,17,19].…”

“…Specifically, dense descriptor tends to have more shape or edge information than conventional methods [19,32]. For example, in [19], the combination of DAISY descriptor with edge map not only produced good classification accuracy, but also reduced the feature computation time based on the collected logo dataset.…”

“…For example, in [19], the combination of DAISY descriptor with edge map not only produced good classification accuracy, but also reduced the feature computation time based on the collected logo dataset. In addition, features such as textures, contours, and edges can be well-represented using saliency map to enhance the distinguishing power of descriptor [19,32].…”

“…Meanwhile, densely computed or sampled based descriptors (i.e. dense SIFT (DSIFT), Phow [17], and DAISY [18,19]) are found to be more promising in resolving image classification issues than typical SIFT descriptor, especially by integrating discriminative techniques [11,18,20]. Essentially, DSIFT descriptor is a fast implementation of SIFT descriptor using densely sampled keypoints of the same orientation and size, whereas Phow descriptor is a Gaussian smoothing filtered implementation of DSIFT.…”