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In classical image classification approaches, low-level features have been used. But the high dimensionality of feature spaces poses a challenge in terms of feature selection and distance measurement during the clustering process. In this paper, we propose an approach to generate visual keyword and combine both visual
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
associated with an image. In our approach, we divide images into small tiles and create visual keywords using a high-dimensional clustering algorithm. These visual keywords act the same as text keywords. One of the challenges of this approach is to identify an appropriate size for visual keywords. In this paper, we report our
In this paper, we propose a novel strategy at an abstract level by combining textual and visual clustering results to retrieve images using semantic keywords and auto-annotate images based on similarity with existing keywords. Our main hypothesis is that images that fall in to the same textcluster can be described
events in soccer video using on-screen texts. The proposed approach is completely automatic and independent to languages since it recommends the users to query events by keywords in image-form which are agents of clusters of stationary on-screen textboxes which are localized and extracted properly by a novel mechanism
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
In document categorization method by using similarity measures based on word vectors, it is important to determine key words to characterize each document. However, conventional methods select the key words based on their frequency or/and particular importance index such as tf-idf. In this paper, we propose a method to characterize each document by using temporal clusters of technical term usages...
Semantic-based information retrieval mechanism that handles the processing, recognition, extraction, extensions and matching of content semantics to achieve the following objectives: i) to analyze and determine the semantic feature of the content and to develop a semantic pattern that represent the semantic features of the content. ii) to analyze user's query and extend its implied semantics through...
keywords in common, then the image is added to an image repository. Additional meta-information are now associated with each image such as caption, cluster features, names of people in the news article, etc. A very large repository containing more than 983k images from 12 million news articles was built using this approach
vocabulary. A group-LASSO regularizer is used to drive as many feature weights to zero as possible. We evaluate the quality of the pruned vocabulary by clustering the data using the resulting feature subset. Experiments on PASCAL VOC 2007 dataset using 5000 visual keywords, resulted in around 80% reduction in the number of
digital library based on topic or concept features. Firstly, documents in a special domain are automatically produced by document classification approach. It integrates the rule-based and statistical method to classify the documents in the large-scale collection. Then, the keywords of each document are extracted using the
This paper is to introduce a new approach to build topic digital library using concept extraction and document clustering. Firstly, documents in a special domain are automatically produced by document classification approach. Then, the keywords of each document are extracted using the machine learning approach. The
A multinet system, comprising SOM's linked via Hebbian connections, has been designed and implemented for automatically annotating and retrieving cell migration images. The collateral compound keywords used in image captions and elsewhere in the text were used to train one SOM and colour moments of the image were used
of the classifier. Our experimental results shows that these measures can improve the classifier's performances, for keywords change too rapidly in emails while address groups are much steadier.
a kernel-selected algorithm based on the lowest similarity, afterwards we get the appropriate keywords to label the topic of each cluster. Finally, experiments on 20Newsgruops email dataset show the validity of our approach and the experimental results also well match the labeled human clustering result.
by combining vectors of the named entities and keywords which can express the center vector of the topic more accurately. Then it deals with topic drift by single-pass clustering and continual modification of the topic center. The result of experiments shows that the new method can reduce the rate of missing and false
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