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      Malware analysis using visualized images and entropy graphs

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          A new method for gray-level picture thresholding using the entropy of the histogram

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            Malware images

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              Rapid biologically-inspired scene classification using features shared with visual attention.

              We describe and validate a simple context-based scene recognition algorithm for mobile robotics applications. The system can differentiate outdoor scenes from various sites on a college campus using a multiscale set of early-visual features, which capture the "gist" of the scene into a low-dimensional signature vector. Distinct from previous approaches, the algorithm presents the advantage of being biologically plausible and of having low-computational complexity, sharing its low-level features with a model for visual attention that may operate concurrently on a robot. We compare classification accuracy using scenes filmed at three outdoor sites on campus (13,965 to 34,711 frames per site). Dividing each site into nine segments, we obtain segment classification rates between 84.21 percent and 88.62 percent. Combining scenes from all sites (75,073 frames in total) yields 86.45 percent correct classification, demonstrating the generalization and scalability of the approach.
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                Author and article information

                Journal
                International Journal of Information Security
                Int. J. Inf. Secur.
                Springer Science and Business Media LLC
                1615-5262
                1615-5270
                February 2015
                April 29 2014
                February 2015
                : 14
                : 1
                : 1-14
                Article
                10.1007/s10207-014-0242-0
                e72e8acb-839c-4874-8ae5-a3ae95a238c3
                © 2015

                http://www.springer.com/tdm

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