Texture and Colour are by far the most important and yet simple features that are most intuitive for describing objects or pictures. MPEG-7 has three main descriptors related to texture. Two of them are related to Homogeneous Texture Descriptor while another is the Edge Histogram Descriptor (EHD). This post shall deal with the analysis of the latter which can be quite useful for scene classification in a video sequence, specially that of sports videos.
C.S. Won et. al. describe the main implementation of the EHD and one of the major applications – Image Retrieval. It also has nice figures to get a clear idea of the procedure.
The idea is that of local processing. The image is divided into 4×4 sub-images. Each sub-image is further divided into smaller image blocks (typically 4×4 pixels). The standard allows for the having vertical, horizontal, diagonal (45 and 135 degrees) and non-directional edges. If the image block is a monotone, no edge is counted. Simple filtering of the image blocks allows to obtain the most prominent edge in the block. A histogram of 5 bins (1 for each edge type) is computed over all the image blocks in the sub-image. This procedure is repeated for all the 16 sub-images and hence we obtain 80 histogram coefficients. The standard proposes non-linear quantisation for the sake of storage (3 bits / coefficient).
Edges are quite important for our visual system, and these coefficients quantify them in a proper way. It is definitely a basis for Image Retrieval – querying for images by example.