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In this paper, we propose a novel image search scheme is contextual image search with keyword input. It is different from conventional image search schemes. it consist of three step process, first one is context extraction to distinguish the image entities of the same name, second step is conceptualization to convert
Internet is becoming an increasingly important platform for ordinary life and work. It is expected that keyword extraction can help people quickly find hot spots on the web, since keywords in a document provide important information about the content of the document. In this paper, we propose to use text clustering
Keywords are indexed automatically for large-scale categorization corpora. Indexed keywords of more than 20 documents are selected as seed words, thus overcoming subjectivity of selecting seed words in clustering; at the same time, clustering is limited to particular category corpora and keywords indexed feature
images with their surrounding text are collected from a few photo forums to support this approach. The entire process is formulated in a divide-and-conquer framework where a query keyword is provided along with the uncaptioned image to improve both the effectiveness and efficiency. This is helpful when the collected data
avoid unnecessary email reading for that a better email management system is required. Here author used fuzzy logic techniques for email clustering. Extract concept and feature, same feature keyword goes into one cluster if a new keyword is found and not matched with any existing cluster than a new cluster is defined for
databases are termed as Web Databases (WDB). Web databases have been frequently employed to search the products online for retail industry. They can be private to a retailer/concern or publicly used by a number of retailers. Whenever the user queries these databases using keywords, most of the times the user will be deviated
title, keyword and link text information to represent the website. Heterogeneous classifiers are then built based on these different features. We propose a principled ensemble classification algorithm to combine the predicted results from different phishing detection classifiers. Hierarchical clustering technique has been
integrating both low level-visual features and high-level textual keywords. Unfortunately, manual image annotation is a tedious process and may not be possible for large image databases. To overcome this limitation, several approaches that can annotate images in a semi-supervised or unsupervised way have emerged. In this paper
We propose an unsupervised approach to segment color images and annotate its regions. The annotation process uses a multi-modal thesaurus that is built from a large collection of training images by learning associations between low-level visual features and keywords. We assume that a collection of images is available
as the services management. Existing methods for Web services clustering mostly focus on utilizing directly key features from WSDL documents, e.g., input/output parameters and keywords from description text. Probabilistic topic model Latent Dirichlet Allocation (LDA) is also adopted, which extracts latent topic features
done on a set of data is chosen to form the basis as done with keywords. If the base data is chosen arbitrarily, it is automatic, whereas some 'knowledge' or 'background' is put in the choice it is adaptive. Statistical features of the images are extracted from the pixel map of the image. The extracted features are
images are to be re-ranked using visual features after the initial text-based search. Here first query keywords are utilize for separating the dataset images into two group of relevant image and irrelevant image then all the images are ranked base on the image different modality of image features as the similar images need
fail when only a tiny amount of labeled data is provided. In this paper, we propose QMAS (Querying, Mining And Summarization of Multi-modal Databases), a fast solution to the following problems: (i) low-labor labeling (L3) - given a collection of images, very few of which are labeled with keywords, find the most suitable
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