The Role of Graduality for Referring Expression Generation in Visual Scenes

International audienceReferring Expression Generation (reg) algorithms, a core component of systems that generate text from non-linguistic data, seek to identify domain objects using natural language descriptions. While reg has often been applied to visual domains, very few approaches deal with the problem of fuzziness and gradation. This paper discusses these problems and how they can be accommodated to achieve a more realistic view of the task of referring to objects in visual scenes

A new approach for content-based image retrieval for medical applications using low-level image descriptors

Content based image retrieval (CBIR) has become an important factor in medical imaging research and is obtaining a great success. More applications still need to be developed to get more powerful systems for better image similarity matching, and as a result getting better image retrieval systems. This research focuses on implementing low-level descriptors to maximize the quality of the retrieval of medical images. Such a research is supposed to set a better result in terms of image similarity matching. In this research a system that uses low-level descriptors is introduced. Three descriptors have been developed and applied in an attempt to increase the accuracy of image matching. The final results showed a qualified system in medical images retrieval specially that the low-level image descriptors have not been used yet in the image similarity matching in the medical field

The role of graduality for referring expression generation in visual scenes

Referring Expression Generation (reg) algorithms, a core component of systems that generate text from non-linguistic data, seek to identify domain objects using natural language descriptions. While reg has often been applied to visual domains, very few approaches deal with the problem of fuzziness and gradation. This paper discusses these problems and how they can be accommodated to achieve a more realistic view of the task of referring to objects in visual scenes.peer-reviewe

Adaptive Multidimensional Fuzzy Sets for Texture Modeling

The modeling of the perceptual properties of texture plays a fundamental role in tasks where some interaction with subjects is needed. In order to face the imprecision related to these properties, fuzzy sets defined on the domain of computational measures of the corresponding property are usually employed. In this sense, the most interesting approaches show that the combination of different measures as reference sets improve the texture characterization. However, the main drawback of these proposals is that they do not take into account the subjectivity associated with human perception. For example, the perception of a texture property may change depending on the user, and in addition, the image context may influence the global perception of a given property. In this paper, we propose to solve these problems by combining the use of several computational measures in a reference set with adaptation to the subjectivity of human perception. To do this, we propose a generic methodology that automatically transforms any multidimensional fuzzy set modeling a texture property to the particular perception of a new user or to the image context. For this purpose, the information given by the user, or extracted from the textures present in the image, are employed

Perception-based fuzzy partitions for visual texture modelling

Visual textures in images are usually described by humans using linguistic terms related to their perceptual properties, like “very coarse”, “low directional”, or “high contrasted”. Computational models with the ability of providing a perceptual texture characterization on the basis of these terms can be very useful in tasks like semantic description of images, content-based image retrieval using linguistic queries, or expert systems design based on low level visual features. In this paper, we address the problem of simulating the human perception of texture, obtaining linguistic labels to describe it in natural language. For this modeling, fuzzy partitions defined on the domain of some of the most representative measures of each property are employed. In order to define the fuzzy partitions, the number of linguistic labels and the parameters of the membership functions are calculated taking into account the relationship between the computational values given by the measures and the human perception of the corresponding property. The performance of each fuzzy partition is analyzed and tested using the human assessments, and a ranking of measures is obtained according to their ability to represent the perception of the property, allowing to identify the most suitable measure