Narrowing Down the Semantic Gap between Content and Context - Agrawal, Rajeev;Grosky, William;Fotouhi, Farshad
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Conventional information retrieval is based solely on text, and the approaches to textual information retrieval have been transplanted into image retrieval in a variety of ways, including the representation of an image as a vector of feature values of different modalities. It has been widely recognized that the image retrieval techniques should become an integration of different modalities, such as color, texture and associated text keywords. To take the cue from text-based retrieval techniques, we construct visual keywords using vector quantization of small sized image tiles. Both visual and…mehr

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Produktbeschreibung
Conventional information retrieval is based solely on text, and the approaches to textual information retrieval have been transplanted into image retrieval in a variety of ways, including the representation of an image as a vector of feature values of different modalities. It has been widely recognized that the image retrieval techniques should become an integration of different modalities, such as color, texture and associated text keywords. To take the cue from text-based retrieval techniques, we construct visual keywords using vector quantization of small sized image tiles. Both visual and text keywords are combined and used to represent an image as a single multimodal vector. We demonstrate the power of these multimodal image keywords for clustering and retrieval of relevant images from a large collection.
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Autorenporträt
Dr. Rajeev Agrawal is working at North Carolina A & T State University, USA. His current research focuses on Anomaly Detection in Computer Network, Healthcare Fraud Detection, and Content-based Image Retrieval. He has published 30 referred journal and conference papers, and 4 book chapters. He is a member of IEEE, ACM, and ASEE.