0957-4174/$ see front matter 2011 Elsevier Ltd. A doi:10.1016/j.eswa.2011.08.165 ⇑ Corresponding author. Tel.: +39 0547 339121; fax E-mail addresses: [email protected] (L. Nanni), (S. Brahnam), [email protected] (A. Lumini). Presented in this paper is a novel feature extractor technique based on texture descriptors. Starting from the standard feature vector representation, we study ...

Behavioral research has demonstrated that the shape and texture of single objects can be processed independently. Similarly, neuroimaging results have shown that an object's shape and texture are processed in distinct brain regions with shape in the lateral occipital area and texture in parahippocampal cortex. Meanwhile, objects are not always seen in isolation and are often grouped together as...

A novel invariant texture classification method is proposed. Invariance to linear/nonlinear monotonic gray-scale transformations is achieved by submitting the image under study to the ranklet transform, an image processing technique relying on the analysis of the relative rank of pixels rather than on their gray-scale value. Some texture features are then extracted from the ranklet images resul...

Texture is one of the important features used in CBIR systems. The methods of characterizing texture fall into two major categories: Statistical and Structural. An experimental comparison of a number of different texture features for content-based image retrieval is presented in this paper. The primary goal is to determine which texture feature or combination of texture features is most efficie...

Automatic recognition of fruits and vegetable products via computer vision is still a difficult task due to complexity of environment. This paper develops a robust fruit and vegetable products recognition method, which integrates color and texture feature based on Harmonic adaptive feature fusion (HAFF) algorithm proposed by this paper. HAFF firstly use exp function to improve AWC, then compute...

Content based image retrieval (CBIR) is an effective method of retrieving images from large image resources. CBIR is a technique in which images are indexed by extracting their low level features like, color, texture, shape, and spatial location, etc. Effective and efficient feature extraction mechanisms are required to improve existing CBIR performance. This paper presents a novel approach of ...

With success of Deep Belief Networks (DBNs) in computer vision, DBN has attracted great attention in hyperspectral classification. Many deep learning based algorithms have been focused on deep feature extraction for classification improvement. Multi-features, such as texture feature, are widely utilized in classification process to enhance classification accuracy greatly. In this paper, a novel...

This paper presents a texture segmentation algorithm based on Discrete Wavelet Frames(DWF) and Cellular Neural Network(CNN). DWF, zero-crossing, texture energy and selective local averaging are used to get a texture feature extraction and t,o form feature images. Each feature image is segmented into parts by several gray range in its gray histogram. Resulting in the number of pixels that confor...

Content-Based Image Retrieval (CBIR) is one of the most active hot spots in the current research field of multimedia retrieval. According to the description and extraction of visual content (feature) of the image, CBIR aims to find images that contain specified content (feature) in the image database. In this paper, several key technologies of CBIR, e. g. the extraction of the color and texture...

A computer aided diagnosis system aiming to classify liver tissue from computed tomography images is presented. For each region of interest five distinct sets of texture features were extracted. Two different ensembles of classifiers were constructed and compared. The first one consists of five Neural Networks (NNs), each using as input either one of the computed texture feature sets or its red...