A generalized Neuro-Fuzzy based Content Based Image Retrieval (CBIR) system is proposed. The system is trained for colour, texture and shape features using General Fuzzy Min-Max Neural Network (GFMNN). Flexibility and robustness is achieved by accepting any number and types of different input features as well with the concept of class labels assigned for each hyperbox. The existing architecture...

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...

The proliferation of content-based image retrieval techniques has highlighted the need to understand the relationship between image clustering based on low-Ievel image features and image clustering made by human users. In conventional image retrieval systems, images are typically characterized by a range offeatures such as color, texture, and shape. However, little is known to what extent these...

Content based image retrieval (CBIR) refers to image content that is retrieved directly, by which the images with features or containing certain contents will be searched in an image database. The main idea of CBIR is to analyze image information by low level features of an image, which includes color, texture, shape and space relationship of objects etc., and to set up feature vectors of an im...

Content-based image retrieval (CBIR) is an important research area with applications to a large amount o f image databases and multimedia information. In CBIR, image is described by several low-level image features, such as color, texture, shape or their combination. The focus o f this paper is on the problem o f shape and texture feature extraction and representation for CBIR. Shape is one o f...

Retrieval of an image is a more effective and efficient for managing extensive image database. Content Based Image Retrieval (CBIR) is a one of the image retrieval technique which uses user visual features of an image such as color, shape, and texture features etc. It permits the end user to give a query image in order to retrieve the stored images in database according to their similarity to t...

Maximizing visual effect is a major problem in real-time animation. A real-time hair animation framework was proposed in [9, 10] based on 2D representation and texture mapping. One problem is that it lacks of the volumetric effect due to its 2D nature. This paper presents a technique using the U-shape strip to solve the problem 1.

We present a new method for classification of malignant and benign clusters of microcalcifications in digital mammograms. A computationally efficient infinite impulse response (IIR) quadrature mirror filter (QMF) bank is used as a tool for extracting texture and shape features. The filter bank splits the input image into four subbands: low-low band, low-high band, high-low band and highhigh ban...

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. In this paper, we have studied an approach based on clustering of the texture features, aiming both to improve the retrieval performance a...