With consumer reviews becoming a mainstream part of ecommerce, a good method of detecting the product or service aspects that are discussed is desirable. This work focuses on detecting aspects that are not literally mentioned in the text, or implicit aspects. To this end, a co-occurrence matrix of synsets from WordNet and implicit aspects is constructed. The semantic relations that exist betwee...

In this paper we present a statistical learning scheme for image classification based on a mixture of old fashioned ideas and state of the art learning tools. We represent input images through large dimensional and usually sparse histograms which, depending on the task, are either color histograms or co-occurrence matrices. Support Vector Machines are trained on these sparse inputs directly to ...

SUMMARY The grey level co-occurrence matrix (GLCM) is a measure of the texture of an image. It describes how often different combinations of pixel brightness values occur in an image. Based on this, several textural attributes can be calculated. These directional attributes can be used to determine isotropic and anisotropic areas. In anisotropic areas the information of directional GLCM-based a...

This paper presents a graph-based method for all-word word sense disambiguation of biomedical texts using semantic relatedness as edge weight. Semantic relatedness is derived from a term-topic co-occurrence matrix. The sense inventory is generated by the MetaMap program. Word sense disambiguation is performed on a disambiguation graph via a vertex centrality measure. The proposed method achieve...

Estimation of the Embedding capacity is an important problem specifically in reversible multi-pass watermarking and is required for analysis before any image can be watermarked. In this paper, we propose an efficient method for estimating the embedding capacity of a given cover image under multi-pass embedding, without actually embedding the watermark. We demonstrate this for a class of reversi...

It has been proven experimentally, that a combination of textual and visual representations can improve the retrieval performance ([20], [23]). It is due to the fact, that the textual and visual feature spaces often represent complementary yet correlated aspects of the same image, thus forming a composite system. In this paper, we present a model for the combination of visual and textual sub-sy...

Most of the existing word embedding models only consider the relationships between words and their local contexts (e.g. ten words around the target word). However, information beyond local contexts (global contexts), which reflect the rich semantic meanings of words, are usually ignored. In this paper, we present a general framework for utilizing global information to learn word and text repres...

In this paper a colour texture segmentation method which unifies region and boundary information is proposed. The algorithm uses a coarse detection of the perceptual (colour and texture) edges of the image to adequately place and initialise a set of active regions. Colour texture of regions is modelled by the conjunction of non-parametric techniques of kernel density estimation (which allow to ...

In this paper we investigate methods for computing similarity of two phrases based on their relatedness scores across all ranks k in a SVD approximation of a phrase/term co-occurrence matrix. We confirm the major observations made in previous work and our preliminary experiments indicate that these methods can lead to reliable similarity scores which in turn can be used for the task of paraphra...

The concept of an algorithm developed for the segmentation of a road border from the content of an image produced by TV camera mounted on a moving vehicle is presented. The extraction of a road boundary is an important step in the context of autonomous vehicle guidance. The segmentation algorithm combines statistical texture descriptors and the ones based on gray level co-occurrence matrix.