Image Description Using Scale-Space Edge Pixel Directions Histogram

Edge directions histograms are widely used as an image descriptor for image retrieval and recognition applications. Edges represent textures and are also representative of the image shapes. In this work a histogram of the edge pixel directions is defined for image description. The edges detected with the canny algorithm will be described in two different scales in four directions. In the lower scale the image is divided into 16 sub-images, and a descriptor with 64 bins results. In the higher scale, as no image division is done because only the most important image features will be present, 4 bins result. A total of 68 bins are used to describe the image in scale-space. Images will be compared using the Euclidean distance between histograms. The provided results will be compared with the ones that result from the use of the histogram in the low scale only. Improved classification using the nearest class mean and neural networks will be used. A higher level semantic annotation, based on this low level descriptor that results from the multiscale image analysis, will be extracted.