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Extensive research has been conducted in an effort to evaluate methods and techniques for image segmentation. However, while most literature has focused on evaluating automatic and semi-automatic algorithms, works evaluating interactive segmentation algorithms are less numerous. Note that interactive segmentation can improve results by adding prior knowledge from users into the process. Although this...
Pairwise and higher order potentials in the Hierarchical Conditional Random Field (HCRF) model play a vital role in smoothing region boundary and extracting actual object contour in the labeling space. However, pairwise potential evaluated by color information has the tendency to over-smooth small regions which are similar to their neighbors in the color space; and the higher order potential associated...
Resources such as quantities of transistors and memory, the level of integration and the speed of components have increased dramatically over the years. Even though the technologies have improved, we continue to apply outdated approaches to our use of these resources. Key computer science abstractions have not changed since the 1960's. Therefore this is the time for a fresh approach to the way systems...
In this paper we present a framework for extraction of Turkish phrases and their concepts. The objective of the study is meeting the requirement of sources for Turkish Semantic Extractions and represent a Turkish sentence at phrase-concept level. The semantic and grammatical analysis of a sentence is a basic content of Natural Language Processing (NLP) which is a branch of Artificial Intelligence...
In an object search scenario with several small objects spread over a large indoor environment, the robot cannot infer about all of them at once. Pruning the search space is highly desirable in such a case. It has to actively select a course of actions to closely examine a selected set of objects. Here, we model the inferences about far away objects and their viewpoint priors into a decision analytic...
Due to heavy clutters and occlusions of complex background, natural images contain complex features in data structure which often cause errors in image classification. In this paper, we propose semi-supervised bi-dictionary learning for image classification with smooth representation-based label propagation (SRLP) which extends reconstruction-based classification in a probabilistic manner. First,...
Document classification is usually more challenging than numerical data classification, because it is much more difficult to effectively represent documents than numerical data for classification purposes. Vector space model (VSM) has been widely used for document representation for classification, in which a document is represented by a vector of feature values based on a bag of words. This paper...
Vocal imitation is widely used in human communication. In this paper, we propose an approach to automatically recognize the concept of a vocal imitation, and then retrieve sounds of this concept. Because different acoustic aspects (e.g., pitch, loudness, timbre) are emphasized in imitating different sounds, a key challenge in vocal imitation recognition is to extract appropriate features. Hand-crafted...
Feature selection is a strategy that aims at making text classifiers more efficient and accurate. In this paper, we proposed a novel feature selection method based on Tibetan grammar for Tibetan classification. Tibetan language express grammatical meaning through the function words and word order, and the function word has large proportions. By analyzing the Tibetan grammar and distribution of part...
Emblematic gesture pictures were presented to subjects as probes in relation to semantically congruent and incongruent sentences to investigate if there is a similar cognitive processing network for congruity as there is with words. Subjects had to perform a simple discrimination task while undergoing EEG recordings. The ERPs elicited by semantically incongruent gestures produced larger N400 and possibly...
In this work a real-time indoor localisation system based on the Viterbi algorithm is developed. This Viterbi principle is used in combination with semantic data to improve the accuracy: i.e., the environment of the object that is being tracked and an adjustable maximum speed. The developed algorithm was verified by simulations and with experiments in a building-wide testbed for sensor and WiFi experiments...
Recommender systems are an integral part of today's internet landscape. Recently the enhancement of recommendation services through Linked Open Data (LOD) became a new research area. The ever growing amount of structured data on the web can be used as additional background information for recommender systems. But current approaches in Linked Data recommender systems (LDRS) miss out on an adequate...
In this paper, we propose a novel approach for reader-emotion categorization using word embedding learned from neural networks and an SVM classifier. The primary objective of such word embedding methods involves learning continuous distributed vector representations of words through neural networks. It can capture semantic context and syntactic cues, and subsequently be used to infer similarity measures...
By virtue of recent developments in machine learning techniques, higher-level information can now to be extracted from big data. To analyze big data, efficient and smart representations of data achieved by using sufficiently fast algorithms, as well as highly accurate results, are important. In this paper, we focus on extracting multiple semantic relations using light-weight processing through the...
Person re-identification is an important computer vision task with many applications in areas such as surveillance or multimedia. Approaches relying on handcrafted image features struggle with many factors (e.g. lighting, camera angle) which lead to a large variety in visual appearance for the same individual. Features based on semantic attributes of a person's appearance can help with some of these...
For the functioning of American democracy, the Lobbying Disclosure Act (LDA), for the very first time, provides data to empirically research interest groups behaviors and their influence on congressional policymaking. One of the main research challenges is to automatically find the topic(s), by short & sparse text classification, in a large corpus of unorganized, semi-structured, and poorly...
Recognizing inference in text (RITE) plays an important role in the answer validation modules for a Question Answering (QA) system. The problem of class imbalance has received increased attention in the machine learning community. In recent years, several attempts have been made on the linguistic phenomena analysis, however, little is known about the effects of imbalanced datasets with linguistic...
The unprecedented growth of data in web, social media and the attempt to make the cognitive process using computers make Sentiment Analysis a challenging and interesting research problem. Sentiment Analysis mainly deals with the process of analyzing the sentiments or feelings from someone's expression or piece of information, and also in discovering the cognitive behavior of humans. The usage of computers...
The opinion conveyed by the user towards a movie can be understood by doing Sentiment Analysis on the movie review. In the current work we focus on Genre Specific Aspect Based Sentiment Analysis of Movie Reviews. Using the aforementioned dataset and considering movie genres like action, comedy, crime, drama and horror, we develop a fine grained unsupervised analysis model using lexicons that are context...
The location of a mobile user is used to deliver context sensitive information like advertisements and deals. Predicting the future possible locations of a mobile user can help target specific services. Nokia provided researchers with data collected from around 200 mobile users over a period of about 2 years for the purpose of research. Previous efforts have attempted either to predict the location...
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