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...

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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence 2007-02, Vol.29 (2), p.300-312
Hauptverfasser: Siagian, C., Itti, L.
Format: Artikel
Sprache:eng
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Zusammenfassung: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
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2007.40