Set Of Visual Elements Research Articles

Proxy Learning of Visual Concepts of Fine Art Paintings from Styles through Language Models

We present a machine learning system that can quantify fine art paintings with a set of visual elements and principles of art. The formal analysis is fundamental for understanding art, but developing such a system is challenging. Paintings have high visual complexities, but it is also difficult to collect enough training data with direct labels. To resolve these practical limitations, we introduce a novel mechanism, called proxy learning, which learns visual concepts in paintings through their general relation to styles. This framework does not require any visual annotation, but only uses style labels and a general relationship between visual concepts and style. In this paper, we propose a novel proxy model and reformulate four pre-existing methods in the context of proxy learning. Through quantitative and qualitative comparison, we evaluate these methods and compare their effectiveness in quantifying the artistic visual concepts, where the general relationship is estimated by language models; GloVe or BERT. The language modeling is a practical and scalable solution requiring no labeling, but it is inevitably imperfect. We demonstrate how the new proxy model is robust to the imperfection, while the other methods are sensitively affected by it.

Multi-model visualization based on integration of data models in semantic network environment

The paper considers the problem of integration of data models for visualization purpose. Integration implies that the set of the data transformations from one model to another is defined and may be realized. A set of visual elements with attached attributes is offered as the final result, this set being an abstraction of the representation of vector graphic information. Conceptual structures of models to be integrated may start acting during computation process required for getting visual representation differently. It is necessary that differently acting conceptual structures (concepts and frames) be parted or separated to provide correct visualization. To have practical separation it is suggested to include in the supporting system the component called semantic separator. In general case data separation is a cognitive task. Use of methods of applicative computational systems gives some advantages. For example, it gives good means for supporting a default mode. Prototype implementations of methods of the semantic separation were tested in developing computer aided law applications.

Modelo aumentado de árbol de decisión utilizando mapas autoorganizados

A key aspect in the process of Knowledge Discovery in Databases (KDD), is the understandability and credibility of models generated by inductive learning schemes. This article explores the application of Self-Organizing Maps (SOM) technique on a model of decision tree, to achieve enhanced visualization of the model. Representations of visual perception model, together with data and patterns are established, based on a schema called VAM-MD, to support exploration and visual analysis efficiently during tuning stage for Data Mining Model. This seeks to answer generic questions about the inner workings of the model and to achieve better understanding. This proposal was implemented through a software prototype, where a set of visual elements may be applied to data from each node in the tree, appropriately selected to complement the visualization of the generated model. Additionally, the user has several mechanisms of interaction that enable for exploration of each component of the model. Finally, the results from a controlled experiment, conducted on two user groups who used the WEKA software and the experimental prototype, for a data mining task on a previously prepared data were analyzed. Preliminary analysis of the results obtained allow, on one hand empirically corroborate the utility of using the SOM technique to visually enhance a decision tree and on the other subjectively estimate their efficiency in supporting the understanding of the model generated.

Painting-to-3D model alignment via discriminative visual elements

This article describes a technique that can reliably align arbitrary 2D depictions of an architectural site, including drawings, paintings, and historical photographs, with a 3D model of the site. This is a tremendously difficult task, as the appearance and scene structure in the 2D depictions can be very different from the appearance and geometry of the 3D model, for example, due to the specific rendering style, drawing error, age, lighting, or change of seasons. In addition, we face a hard search problem: the number of possible alignments of the painting to a large 3D model, such as a partial reconstruction of a city, is huge. To address these issues, we develop a new compact representation of complex 3D scenes. The 3D model of the scene is represented by a small set of discriminative visual elements that are automatically learned from rendered views. Similar to object detection, the set of visual elements, as well as the weights of individual features for each element, are learned in a discriminative fashion. We show that the learned visual elements are reliably matched in 2D depictions of the scene despite large variations in rendering style (e.g., watercolor, sketch, historical photograph) and structural changes (e.g., missing scene parts, large occluders) of the scene. We demonstrate an application of the proposed approach to automatic rephotography to find an approximate viewpoint of historical paintings and photographs with respect to a 3D model of the site. The proposed alignment procedure is validated via a human user study on a new database of paintings and sketches spanning several sites. The results demonstrate that our algorithm produces significantly better alignments than several baseline methods.

Economic Conditions and Changes in the Financial Valuation of a Brand (Coyuntura Económica y Variación en la Valoración Financiera de una Marca)

It is increasingly common to use concepts such as intangible assets or intellectual capital. It has been frequent buying and selling companies for prices much higher value than book value. In this context, it seems relevant to study how to determine the brand financial value (global brand)-understood as a set of visual elements, intellectual property rights and all organizational elements that have a business- as well as its role increasing the market price of the company. The theoretical development is illustrated by a case study in particular: Adolfo Dominguez. It is noted that the assessments, using the same pattern but changing some parameters, vary in a range between about 43 and more than 61 million Euros. It is therefore concluded that, even in the most objective models appear subjective and economic circumstances that influence the market by providing higher valuations in good times and lower valuations in times of crisis.