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This paper proposes a method for keyword spotting in offline Chinese handwritten documents using a statistical model. On a text query word, the method measures the similarity between the query word and every candidate word in the document by combining a character classifier and four classifiers characterizing the
This paper proposes an unsupervised two-stage approach to automatically extract keywords from spoken documents. In the first stage, for each candidate term we compute a topic coherence and term significance measure (TCS) based on probabilistic latent semantic analysis (PLSA) models. In the second stage, we take the
paper, we propose the automatic keyword extraction system and Thai website categorization system which can automatically update the dictionary and categorize website in Thai. The dictionary is a collection of vector which is created from the automatic keyword extraction system. The result in term of accuracy shows that our
Word posterior probability has been widely used as the confidence estimation of automatic speech recognition (ASR) systems and has been proved to be quite effective in related applications such as keyword search. However, word posterior probability tends to overestimate the true probability of a hypothesis, as it is
This paper presents a keyword extraction method of web pages based on domain thesaurus. The method extracts keywords from web pages based on traditional statistic features, such as frequency and location, and it also evaluates the weight of candidate keywords combining with their relation of domain thesaurus. This
In this paper we present our current work on automatic speaker recognition using keyword-conditioned phone N-gram modeling. We propose the use of contextual information around keywords in modeling a speaker's pronunciation characteristics at a phonetic level. Our approach is to add time margins around keywords when
Keyword extraction is an automated process that collects a set of terms, illustrating an overview of the document. The term is defined how the keyword identifies the core information of a particular document. Analyzing huge number of documents to find out the relevant information, keyword extraction will be the key
This research is concerned with the table based KNN as the approach to the keyword extraction task. The keyword extraction task is viewed as an instance of word classification, and it is discovered that encoding words into tables improved the word categorization performance. In this research, words are encoded into
. In view of the traditional feature extraction method based on binary program, this paper presents a method for feature extraction of JAVA source code. The method uses the Keywords Correlation Distance to compute the correlation between key codes such as API calls, Android permissions, the common parameters, and the
very large when a dense grid is used where the histograms are computed and combined for many different points. The current dominating solution to this problem is to use a clustering method to create a visual codebook that is exploited by an appearance based descriptor to create a histogram of visual keywords present in an
Use of semantic content is one of the important tasks in image analysis, which needs to be addressed for improving image retrieval effectiveness. We present a method to assign multiple keywords to image using SVMs. Images are divided into three-level regions called global image, semi-global images and sub-images. For
This paper presents an audio keywords detection method for highlight retrieval in basketball video. The keywords contain shoes squeaking sound, speech, cheer, long whistle and short whistle, which correspond to basketball game events. After feature analysis, the Simple Excellent Feature Combination based on Pearson
Searching images their semantic is an active problem in multimedia image retrieval. Many researchers have attempted to improve semantic models by using high-level concept based on keyword annotation. However, the annotation is tedious, in consistent, and erroneous. The retrieval process of such approaches is done by
list products based on keywords. As the inherent limitation, keyword browsing makes it difficult to find the exact products that human being desire. In this paper, we propose a visual search algorithm based on contour salient. The proposed approach extracts the object edge using Canny edge detector, and then chooses the
In this paper we propose the method that extracts the semantic keyword from digital images automatically using color and texture features. The image semantic keyword is widely used in research area like image retrieval, categorization, annotation, management. The method consists of two steps: feature extraction and
The rapid development of modern technology has resulted in large amount of electronically available information in articles and patents. The search engines are programs that searches the documents in a database correspond to queries specified by the user. Searching by using keywords for millions of documents will not
In this paper we propose an approach for Chinese question analysis and answer extraction. A general question analysis process contains keyword extraction and question classification. Question classification plays a crucial role in automatic question answering. To implement the question classification, we have carried
provide simple message analysis features such as browsing and simple keyword-based searching of the recorded messages. In this paper, we propose a system, called IMAnalysis, that supports intelligent chat message analysis using text mining techniques. The IMAnalysis system provides functions on chat message retrieval, social
To exploit co-occurrence patterns among features and target semantics while keeping the simplicity of the keyword-based visual search, a novel reranking methods is proposed. The approach, ordinal reranking, reranks an initial search list by utilizing the co-occurrence patterns via the ranking functions such as ListNet
Semantic soccer video analysis has attracted more and more attention recently. In this paper, we present a football event detection method by using multiple feature extraction and fusion. Instead of using low-level features, the proposed method is built upon visual, auditory features, text and audio keywords
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