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In this paper, a novel semantic segmentation model based on aggregated features and contextual information is proposed. Given an RGB-D image, we train a support vector machine (SVM) to predict initial labels using aggregated features, and then optimize the predicted results using contextual information. For aggregated features, the local features on regions are extracted to capture visual appearance...
Wireless capsule endoscopy video summarization (WCE-VS) is highly demanded for eliminating redundant frames with high similarity. Conventional WCE-VS methods extract various hand-crafted features as image representations. Researches show that such features only reflect the low-level characteristics of single frame and essentially are not effective to capture the semantic similarity between WCE frames...
Modern young people (“digital natives”) have grown in an era dominated by new technologies where communications are pushed to quite a real-time level, and pose no limits in establishing relationships with other people or communities. However, the speed of evolution does not allow young people to split consciously acceptable behaviors from potentially harmful ones and a new phenomenon known as cyber...
Deep learning-based models have recently been widely successful at outperforming traditional approaches in several computer vision applications such as image classification, object recognition and action recognition. However, those models are not naturally designed to learn structural information that can be important to tasks such as human pose estimation and structured semantic interpretation of...
During the natural language communication, meaning understanding is the complex task that humans learn from their childhood but to automate this process of meaning understanding for computers has great real world applications. Simple text processing tasks are not enough to uncover the meaning from given unstructured natural language text. Our current research focuses on the issues pertaining to the...
The two main problems of biomedical event extraction are trigger identification and argument detection which can both be considered as classification problems. In this paper, we propose a distributed representation method, which combines context, consisted by dependency-based word embedding, and task-based features represented in a distributed way on deep learning models to realize biomedical event...
While several relation extraction algorithms have been developed in the past decade, mainly in the English language, only few researchers target the Arabic language owing to its complexity and rich morphology. This paper proposes a semi-supervised pattern-based bootstrapping technique to extract Arabic semantic relation that lies between entities. In order to enhance the performance to suit the morphologically...
This paper presents a method named SoSVMRank, which integrates the social information of a Web document to generate a high-quality summarization. In order to do that, the summarization was formulated as a learning to rank task, in which the order of a sentence or comment was determined by its informative information. The informative information was measured by a set of local and social features in...
In order to manage and organize information on the web, we propose a novel web page classification strategy integrating topic model and SVM. We use topic model to harness the implicit information on web pages for feature extraction. Accuracy of the strategy is 84.15%, 2.23% superior to the traditional classification strategy based on CHI.
In this paper the problem of complex event detection is addressed. Existing event detection methods are limited to features that are extracted from local spatial or spatio-temporal patches from the videos. However, this makes the model more vulnerable to the events that have similar concepts with different actions e.g. "Open drawer" and "Open cupboard". Furthermore, current methods...
Documents indexing is the main step in a conventional document classification or information retrieval framework. This study aims to highlight the influence of features' type on the efficiency of a classification system. Empirical results on Arabic dataset reveal that the choice of extracted feature's type has a significant impact on conserving semantic information and improving classification accuracy,...
Object semantic reduces the semantic gap in Content Based Image Retrieval (CBIR). In recent years, numerous methods for object semantic categorization have been proposed. Semantic segmentation is a key factor affecting the accuracy of object semantic categorization. The existing semantic segmentation methods usually chose pixel or super-pixel as the processing input. But the information contained...
In this paper, we describe our practical efforts for applying speech emotion recognition(SER) in customer care scenarios. We systematically analyze the challenges we observe in our data, which are very different from speech emotion databases uttered by actors. Our contributions are two-fold. One, we propose a 2-level framework to measure the customers satisfaction score on the conversation level....
FAQs are the lists of common questions and answers on particular topics. Today one can find them in almost all web sites on the internet and they can be a great tool to give information to the users. Questions in FAQs are usually identified by the site administrators on the basis of the questions that are asked by their users. While such questions can respond to required information about a service,...
The traditional text classification methods usually follow this process: first, a sentence can be considered as a bag of words (BOW), then transformed into sentence feature vector which can be classified by some methods, such as maximum entropy (ME), Naive Bayes (NB), support vector machines (SVM), and so on. However, when these methods are applied to text classification, we usually can not obtain...
In still images, multi-scale regions contain rich information of different granularity. However, only semantically meaningful regions provide auxiliary cues for action recognition. Moreover, regions at different scales contribute differently. Motivated by the two observations, we propose an approach that is composed of three components: 1) detecting semantic region candidates at multiple scales, 2)...
Lexical inference problem is a significant component of some recent core AI and NLP research problems like machine reading and textual entailment. In this paper, we propose method utilizing the Probabilistic Soft Logic (PSL) model for Chinese lexical inference. The proposed PSL model not only can integrate two complementary traditional methods, i.e., the lexical-knowledge-based method and the distributional...
Sentiment Classification refers to the computational techniques for categorizing whether the sentiments of a text are positive or negative. Sentiment Classification approaches would suffer due to Negation Modifiers. Negation Modifiers, like word “not” modify the meaning of the associated word. Handling Negation Modifier is important as they may modify the Sentiment conveyed by the associated word...
Microblog post has been a hot research source for emotion classification in recent years. However, due to bloggers' free narrative style and topics' timeliness, the data from microblog post is usually implicit and imbalanced. In this paper, the problems of emotion classification in Chinese microblog posts are solved in a hierarchical way using a knowledge-based topic model and Support Vector Machine(SVM)...
In order to bridge the gap between objective levels of audio frequency characters and subject emotional rang inequality, and to avoid subjective feeling of the listener marked the emotion categories by music, this paper presents a connotative space for music affective recommendation. Using the semantic polar scale, the connotative space of music is shaped by musical strength, speed and intensity....
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