This paper presents a new dynamic classifier selection approach for hyperspectral image classification, in which both spatial and spectral information are used to determine a pixel’s label once the remaining classified pixels’ neighborhood meets the threshold. For volumetric texture feature extraction, a volumetric gray level co-occurrence matrix is used; for spectral feature extraction, a mini...

A key task in an extraction system for query-oriented multi-document summarisation, necessary for computing relevance and redundancy, is modelling text semantics. In the Embra system, we use a representation derived from the singular value decomposition of a term co-occurrence matrix. We present methods to show the reliability of performance improvements. We find that Embra performs better with...

1047-3203/$ see front matter 2010 Elsevier Inc. A doi:10.1016/j.jvcir.2010.08.005 q The paper is the extension of our previous w Workshop on Web-Scale Multimedia Corpus, 2009. ⇑ Corresponding author. E-mail addresses: [email protected] (S. Gao), as [email protected] (X. Cheng). This paper focuses on improving the semi-manual method for web image concept annotation. By sufficiently studying ...

-A relative entropy based approach to image thresholding was proposed recently. It was demonstrated that this method was successful for image thresholding. Relative entropy is a member of the class of Ali-Silvey distance measures. In this paper we generalize the relative entropy based approach and present image thresholding algorithms based on the class of Ali-Silvey distance measures. A number...

This paper proposed an efficient approach, for weather Radar image classification and filtering, which is based on the combination of the textural analysis and support vector machine (SVM). Since the images are formed by two kinds of echoes, precipitations (rainfalls) and ground echoes (clutter), caused by earth's surface, thus, our purpose is to preserve precipitations and eliminate ground ech...

We present a novel ordinal co-occurrence matrix framework for the purpose of content-based texture retrieval. Several particularizations of the framework will be derived and tested for retrieval purposes. Features obtained using the framework represent the occurrence frequency of certain ordinal relationships at different distances and orientations. In the ordinal co-occurrence matrix framework...

A two tier system for MR image segmentation is proposed. The first stage involves probabilistically classifying the pixels of the input image based on second order grey level statistics obtained from the co-occurrence matrix. These probabilities then form the input to a multi-layer perceptron, allowing contextual constraints to be applied to local areas of the image thereby improving the spatia...

In this paper, a new method for extracting primitives of a regular texture via wavelet transform is provided. Each primitive is restricted to be a parallelogram. The main work of the proposed method is to extract the two displacement vectors that form the primitive of a regular texture. A contrast-based criterion is used to select appropriate subimages obtained from wavelet transform for detect...

In this paper we present a method for segmentation of documents image with complex structure. This technique based on GLCM (Grey Level Co-occurrence Matrix) used to segment this type of document in three regions namely, 'graphics', 'background' and 'text'. Very briefly, this method is to divide the document image, in block size chosen after a series of tests and then applying the co-occurrence ...

We present a constraint adaptive image segmentation technique designed to achieve the combined purposes of color image coding/compression, indexing and content-based retrieval. An image is segmented into homogeneous squared regions of variable sizes such that it can be encoded very emciently [7]. From the segmented image, we derive an effective and efficient image content description feature te...