Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations
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Abstract—Many machine vision applications, such as compression, pictorial database querying, and image understanding, often need to analyze in detail only a representative subset of the image, which may be arranged into sequences of loci called regions-of-interest, ROIs. We have investigated and developed a methodology that serves to automatically identify such a subset of aROIs (algorithmically detected ROIs) using different Image Processing Algorithms, IPAs, and appropriate clustering procedures. In human perception, an internal representation directs top-down, context-dependent sequences of eye movements to fixate on similar sequences of hROIs (human identified ROIs). In this paper, we introduce our methodology and we compare aROIs with hROIs as a criterion for evaluating and selecting bottom-up, context-free algorithms. An application is finally discussed.
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Index Terms:
Eye movements, scanpath theory, regions of interest identification and comparison.
Citation:
Claudio M. Privitera, Lawrence W. Stark, "Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 9, pp. 970-982, Sept. 2000, doi:10.1109/34.877520
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