Tagr, an automatic image tagging system is introduced, that assigns content description tags to arbitrary photographic and non-photographic images, using massive online image libraries, such as Flickr for training. Combining techniques from machine learning and content-based image retrieval, a flexible system is introduced to automatically tag a wide variety of images from various sources. A nu...

Proposed a method of combining two-dimensional Gabor transforms and invariant moments to extract palmprint feature, and using multilayer towards feedback neural network for training palmprint images to recognize. This method first pretreated the collected palmprint images and got the region of interest (ROI), then constructed a set of Gabor filters to get ROI eigenvectors, combined with the pal...

Recent time of flight cameras deliver range images (2.5D) in realtime, and can be considered as a significant improvement when compared to conventional (2D) cameras. However, the range map produced has only a limited extent, and suffers from occlusions. In this paper, we investigate fusion methods for partially overlapping range images, aiming to address the issues of lateral field of view exte...

Multiple imputation is a common approach for handling missing data. It allows users to make valid inferences using standard complete-data methods with simple combining rules. A variation is to partition the missing data into two portions and conduct the imputation in two stages. We review two-stage multiple imputation and existing inferential methods and derive an alternative reference F -distr...

Expert systems applications that involve uncertain inference can be represented by a multidimensional contingency table. These tables offer a general approach to inferring with uncertain evidence, because they can embody any form of association between any number of pieces of evidence and conclusions. (Simpler models may be required, however, if the number of pieces of evidence bearing on a con...

Abstract Multiple imputation is a commonly used approach to deal with missing values. In this approach, an imputer repeatedly imputes the missing values by taking draws from the posterior predictive distribution for the missing values conditional on the observed values, and releases these completed data sets to analysts. With each completed data set the analyst performs the analysis of interest...

In this paper cascaded reduction and growing of result sets is introduced as a principle for combining the results of different object detectors. First, different candidate operating points are selected for each object detection algorithm. This procedure is based on the analysis of precision and recall of the individual methods. Selecting an appropriate operating point prior to fusion is import...

Domain-specific Internet of Things (IoT) applications are becoming more and more popular. They process data coming from sensor measurements. Adding semantic annotations to the sensory observations and measurements can allow to reason on data via logical rules. Stemming from Linked Open Data and Linked Open Vocabularies, we have designed sensor-based Linked Open Rules (S-LOR). S-LOR allows explo...

In this work we present a system for the automatic annotation of opinions in Spanish texts. We focus mainly in the definition of a TFS-style model for the predicates of opinion and their arguments, in the creation of a lexicon of opinion predicates and in two additional variants for identifying the source of opinions. The original system extracts opinions and all its elements (predicate, source...

Unlike fixed combining rules, the trainable combiner is applicable to ensembles of diverse base classifier architectures with incomparable outputs. The trainable combiner, however, requires the additional step of deriving a second-stage training dataset from the base classifier outputs. Although several strategies have been devised, it is thus far unclear which is superior for a given situation...