A survey of browsing models for content based image retrieval |
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Authors: | Daniel Heesch |
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Affiliation: | (1) Department of Electrical and Electronic Engineering, Imperial College London, SW7 2AZ London, UK |
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Abstract: | The problem of content based image retrieval (CBIR) has traditionally been investigated within a framework that emphasises
the explicit formulation of a query: users initiate an automated search for relevant images by submitting an image or draw
a sketch that exemplifies their information need. Often, relevance feedback is incorporated as a post-retrieval step for optimising
the way evidence from different visual features is combined. While this sustained methodological focus has helped CBIR to
mature, it has also brought out its limitations more clearly: There is often little support for exploratory search and scaling
to very large collections is problematic. Moreover, the assumption that users are always able to formulate an appropriate
query is questionable. An effective, albeit much less studied, method of accessing image collections based on visual content
is that of browsing. The aim of this survey paper is to provide a structured overview of the different models that have been
explored over the last one to two decades, to highlight the particular challenges of the browsing approach and to focus attention
on a few interesting issues that warrant more intense research.
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Keywords: | Image retrieval CBIR Human-computer interaction Data visualization Browsing Networks Clustering Dimensionality reduction |
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