In this paper, we discover and annotate visual attributes for the COCO dataset. With the goal of enabling deeper object understanding, we deliver the largest attribute dataset to date. Using our COCO Attributes dataset, a fine-tuned classification system can do more than recognize object categories – for example, rendering multi-label classifications such as “sleeping spotted curled-up cat” ins...

This report deals with a Bayesian neural network in a classiier context. In our network model, the units represent stochastic events, and the state of the units are related to the probability of these events. The basic Bayesian model is a one-layer neural network, which calculates the posterior probabilities of events, given some observed, independent events. The formulas underlying this networ...

Shape is one of the most important visual attributes used to characterize objects, playing a important role in pattern recognition. There are various approaches to extract relevant information of a shape. An approach widely used in shape analysis is the complexity, and Fractal Dimension and Multi-Scale Fractal Dimension are both well-known methodologies to estimate it. This papers presents a co...

We extend the development of collocation methods within the framework of Isogeometric Analysis (IGA) to multi-patch NURBS configurations, various boundary and patch interface conditions, and explicit dynamic analysis. The methods developed are higher-order accurate, stable with no hourglass modes, and efficient in that they require a minimum number of quadrature evaluations. The combination of ...

This paper addresses the multi-attributed graph matching problem considering multiple attributes jointly while preserving the characteristics of each attribute. Since most of conventional graph matching algorithms integrate multiple attributes to construct a single attribute in an oversimplified way, the information from multiple attributes are not often fully exploited. In order to solve this ...

In this paper, modified Approach for classifying Multi-dimensional data cube is constructed. It explores data cubes in large Multi-Dimensional Schema. Numerical and Nominal attributes are categorized based on Principal Component Analysis. Semantic relationships are identified by applying Multidimensional scaling. Additionally, AR is integrated for finding the inserting measures. Many algorithms...