Image retrieval is an important research field which has received great attention in the last decades. In this paper, we present an approach for the image retrieval based on the combination of text-based and content-based features. For text-based features, keywords and for content-based features, color and texture features have been used. Query in this system contains some keywords and an input...

Steganography is the profession of concealing a privacy message within a stated medium like image, audio or video. The main issue regarding every steganography technique is the changes that occur in the cover image after the data embedding procedure. We are using the source texture image for embedding privacy message in diversion with the cover image. This technique grants us to de-embedding th...

In this paper, a novel unsupervised segmentation framework for texture image queries is presented. The proposed framework consists of an unsupervised segmentation method for texture images, and a multi-filter query strategy. By applying the unsupervised segmentation method on each texture image, a set of texture feature parameters for that texture image can be extracted automatically. Based upo...

texture detection, texture analysis, texture synthesis, image analysis An algorithm is presented for the detection of textured areas in digital images. Texture detection has potential application to image enhancement, tone correction, defect detection, content classification and image segmentation. For example, texture detection may be combined with a color model and other descriptors to detect...

This paper proposes an image retrieval method based on multi-feature similarity score fusion using genetic algorithm. In recent years, research and development in the content-based image retrieval has mainly focused n image features, such as color, shape, texture and spatial relationships. In addition, many content-based image retrieval systems and methods have been developed for various applic...

Since image processing aimed at reducing image noise can also remove important texture, standard methods for evaluating the capture and retention of image texture are currently being developed. Concurrently, the evolution of the intelligence and performance of camera noise-reduction (NR) algorithms poses a challenge for these protocols. Many NR algorithms are ‘content-aware’, which can lead to ...

This paper uses theory, simulation, and laboratory experiment, to show that directional illumination, used during the image acquisition process, acts as a directional filter of three dimensional texture. It is shown that the directional characteristics of image texture are not intrinsic to the physical texture being imaged, as they are affected by the direction of the illumination. This result ...

Texture classification is an active topic in image processing which plays an important role in many applications such as image retrieval, inspection systems, face recognition, medical image processing, etc. There are many approaches extracting texture features in gray-level images such as local binary patterns, gray level co-occurence matrixes, statistical features, skeleton, scale invariant fe...

Digital image and video libraries require new algorithms for the automated extraction and indexing of salient image features. Texture features provide one important cue for the visual perception and discrimination of image content. In this paper we propose a new approach for automated content extraction that allows for e cient database searching using texture features. The algorithm automatical...