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With the rapid development of B2C e-commerce and the popularity of online shopping, the Web storages huge number of product reviews comment by customers. Product reviews contain subjective feelings of customers who have used some products, more and more customers browse a large number of online reviews in order to know other customers word-of-mouth of product and service to make an informed choice...
Multimodal recognition has recently become more attractive and common method in multimedia information retrieval. In many cases it shows better recognition results than using only unimodal methods. Most of current multimodal recognition methods still depend on unimodal recognition results. Therefore, in order to get better recognition performance, it is important to choose suitable features and classification...
Recognition of human-human interactions is one of the most important topics since it has great scientific importance and many potential practical applications such as surveillance, and automatic video indexing. Previous approaches have only concentrated on classification and put less effort into localization of human interactions. In addition, they rely on hand-designed features (e.g. SIFT, HOG),...
In this paper we propose a task-driven progressive part localization (TPPL) approach for fine-grained object recognition. Most existing methods follow a two-step approach which first detects salient object parts to suppress the interference from background scenes and then classifies objects based on features extracted from these regions. The part detector and object classifier are often independently...
Identifying different types of damage is very essential in times of natural disasters, where first responders are flooding the internet with often annotated images and texts, and rescue teams are overwhelmed to prioritize often scarce resources. While most of the efforts in such humanitarian situations rely heavily on human labor and input, we propose in this paper a novel hybrid approach to help...
Traditional customer needs elicitation methods are often time and cost consuming due to the linguistic analysis of customer needs. Furthermore, many of them are unable to identify latent customer needs, such as interviews and focus groups. This paper proposes a new paradigm of customer needs elicitation based on sentiment analysis of individual product attributes of online product reviews. Support...
Customer Reviews for Product and allied services has become a de-facto for Vendors selling products online. Customer reviews for a product is becoming more important day by day as e-commerce industry grows imminently. For a renowned product, the number of customer reviews can run to many counts. This makes the buying decision for the customer more complex. The crux of this research work is to do a...
Twitter sentiment analysis offers organizations an ability to monitor public feeling towards the products and events related to them in real time. Most existing researches for Twitter sentiment analysis are focused on the extraction of sentiment feature of lexical and syntactic feature that are expressed explicitly through words, emoticons, exclamation marks etc, although sentiment implicitly expressed...
We carry out image labeling based on probabilistic integration of local, middle and global information. Local information is effective for capturing color and texture pattern. Middle information is obtained from patches which are larger than local regions and is able to incorporate context information. Global information obtained from an entire image helps to decide the presence of categories in the...
This paper develops a method to learn very few discriminative part detectors from training videos directly, for action recognition. We hold the opinion that being discriminative to action classification is of primary importance in selecting part detectors, not just intuitive. For this purpose, part selection based on feature selection is proposed, employing SVM method. Firstly, large number of candidate...
This paper presents an automatic event detection system fusing low and mid level features for soccer videos. We first employ an improved approach for Shot Boundary Detection with color and our mean-gradient feature. Then we classify the shots into two view types. We also perform a template-based replay detection for each shot. Play-break sequences are then generated using a rule-based method. We devise...
Movie summarization aims at condensing a full-length movie to a significantly shortened version that still preserves the movie's major semantic content. In this paper, we propose a learning-based movie summarization framework via role-community social network analysis and feature fusion. In our framework, scene-based movie summarization is formulated as a 0–1 knapsack problem, where the scene attention...
In this paper, we investigate the task of paraphrase identification in Vietnamese documents, which identify whether two sentences have the same meaning. This task has been shown to be an important research dimension with practical applications in natural language processing and data mining. We choose to model the task as a classification problem and explore different types of features to represent...
Emotion is a vital component in various Affective Computing areas such as opinion mining, sentiment analysis, e-learning applications, human-computer interaction and humor recognition. In this paper, we propose a two-stage approach for detecting emotions on Indonesian tweets. In the first stage, we extract emotion-bearing tweets from a huge number of raw tweets. In the second stage, all the extracted...
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...
The semantic relations between entities in documents play vital roles in many applications such as information extraction, and information retrieval. The problem of extracting the semantic relations in Vietnamese documents is more difficult because Vietnamese textual analysis tools are in progress. In this paper, we propose an approach based on syntactic dependency feature between provisions in each...
The sensory experience of watching a movie, links input from both sight and hearing modalities. Yet traditionally, the motion picture rating system largely relies on the visual content of the film, to make its informed decisions to parents. The current rating process is fairly elaborate. It requires a group of parents to attend a full screening, manually prepare and submit their opinions, and vote...
Author attribution has grown into an area that is more challenging from the past decade. It has become an inevitable task in many sectors like forensic analysis, law, journalism and many more as it helps to detect the author in every documentation. Here unigram/bigram features along with latent semantic features from word space were taken and the similarity of a particular document was tested using...
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