NDLI: A Framework for Interactive Content-Based Image Retrieval

Content Provider	IEEE Xplore Digital Library
Author	Monir, S.M.G. Hasnain, S.K.
Copyright Year	2005
Description	Author affiliation: Fast-Nat. Univ., Karachi (Monir, S.M.G.)
Abstract	With the exponential increase in the size of digital image databases in the past few years, traditional way of manually annotating the images with text and then using text-based queries for image retrieval has been giving way to content-based image retrieval (CBIR) systems that use the visual contents of the images to automatically index and retrieve digital images. However, there is always a gap between high-level human perception of an image and the low-level image features used to describe its contents. This gap between low-level image features and semantic image content is the major bottleneck faced by traditional CBIR systems. Modern CBIR systems overcome this problem by using interactive learning, bringing the user in the loop. Such systems learn from feedbacks given by the user about the relevance or irrelevance of the current retrieval results. This paper presents a framework for interactive content-based image retrieval. Considering relevance feedback as a learning problem, a learning machine based on radial basis functions (RBF) neural networks (NN) is implemented and the system has been tested for its effectiveness using a database of 10,000 images
Starting Page	1
Ending Page	4
File Size	4111487
Page Count	4
File Format	PDF
ISBN	0780394291
DOI	10.1109/INMIC.2005.334425
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-12-24
Publisher Place	Pakistan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image retrieval Content based retrieval Information retrieval Digital images Image databases Neurofeedback Machine learning Neural networks Visual databases Indexes
Content Type	Text
Resource Type	Article

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