RT Conference_Proceedings
T1 Improving Scene Recognition Through Visual Attention
A1 López García, Fernando
A1 García Díaz, Antón
A1 Fernández Vidal, Xosé Ramón
A1 Pardo López, Xosé Manuel
A1 Dosil Lago, Raquel
A1 Luna, David
K1 Visual attention
K1 Scene recognition
K1 SIFT features
K1 Nearest Neighbor rule
K1 Recognition performance
AB In this paper we study how the use of a novel model of bottom-up saliency (visual attention), based on local energy and color, can significantly accelerate scene recognition and, at the same time, preserve the recognition performance. To do so, we use a mobile robot-like application where scene recognition is performed through the use of SIFT features to characterize the different scenarios, and the Nearest Neighbor rule to carry out the classification. Experimental work shows that important reductions in the size of the database of prototypes can be achieved (17.6% of the original size) without significant losses in recognition performance (from 98.5% to 96.1%), thus accelerating the classification task.
PB Springer
YR 2009
FD 2009
LK http://hdl.handle.net/10347/32709
UL http://hdl.handle.net/10347/32709
LA eng
NO López-García, F., García-Díaz, A., Fdez-Vidal, X.R., Pardo, X.M., Dosil, R., Luna, D. (2009). Improving Scene Recognition through Visual Attention. In: Araujo, H., Mendonça, A.M., Pinho, A.J., Torres, M.I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2009. Lecture Notes in Computer Science, vol 5524. Springer, Berlin, Heidelberg.
NO Work financially supported by the Ministry of Education and Science of the Spanish Government (research projects AVISTA TIN2006-08447 and DPI2007-66596-C02-01), and the Government of Galicia (research project PGIDIT07PXIB206028PR).
DS Minerva
RD 24 abr 2026