music videos for MTV, and even

The aim of this chapter is to give experienced and new practitioners in image analysis and computer vision an overview and a quick reference to the “galaxy” of features that exist in the field of texture analysis. Clearly, given the limited space, only a corner of this vast galaxy is covered here

Texture feature evaluation for segmentation of historical

Abstract—Texture features that are based on the local power spectrum obtained by a bank of Gabor filters are compared. The features differ in the type of nonlinear post-processing which is applied to the local power spectrum. The following features are considered: Gabor energy, complex moments

Distinctive texture features from perspective-invariant keypoints

We consider the problem of zone class$cation in document image processing. Document blocks are la-belled as text or non-text using texture features de-rived from a feature based interaction map (FBIM), a recently introduced general tool for texture analysis [3, 41. The zone classijication procedure

Content-based image retrieval has received significant attention in recent years and many image retrieval systems have been developed based on image contents. In such systems, the well-known features to describe an image content are color, shape and texture.

effective content-based image retrieval (CBIR) systems involves the combination of image creation, storage, security, transmission, analysis, evaluation feature extraction, and feature combination in order to store and retrieve images effectively. The goal of CBIR systems is to support image retrieval based

The authors wish to acknowledge the invaluable help and

Texture, the pattern of information or arrangement of the structure found in an image, is an important feature of many image types. In a general sense, texture refers to surface characteristics and ap-pearance of an object given by the size, shape, density, arrange-ment, proportion of its