López García, FernandoGarcía Díaz, AntónFernández Vidal, Xosé RamónPardo López, Xosé ManuelDosil Lago, RaquelLuna, David2024-02-122024-02-122009López-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.http://hdl.handle.net/10347/32709In 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.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Visual attentionScene recognitionSIFT featuresNearest Neighbor ruleRecognition performanceconference output10.1007/978-3-642-02172-5_4open access