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Low-Level Spatiochromatic Grouping for Saliency Estimation
Nov. 2013 (vol. 35 no. 11)
pp. 2810-2816
N. Murray, Xerox Res. Centre Eur., Meylan, France
M. Vanrell, Centre de Visio per Computador, Barcelona, Spain
X. Otazu, Centre de Visio per Computador, Barcelona, Spain
C. A. Parraga, Centre de Visio per Computador, Barcelona, Spain
We propose a saliency model termed SIM (saliency by induction mechanisms), which is based on a low-level spatiochromatic model that has successfully predicted chromatic induction phenomena. In so doing, we hypothesize that the low-level visual mechanisms that enhance or suppress image detail are also responsible for making some image regions more salient. Moreover, SIM adds geometrical grouplets to enhance complex low-level features such as corners, and suppress relatively simpler features such as edges. Since our model has been fitted on psychophysical chromatic induction data, it is largely nonparametric. SIM outperforms state-of-the-art methods in predicting eye fixations on two datasets and using two metrics.
Index Terms:
solid modelling,estimation theory,image colour analysis,image enhancement,eye fixations,low-level spatiochromatic grouping,saliency estimation,saliency model,SIM,saliency by induction mechanisms,low-level spatiochromatic model,chromatic induction phenomena,low-level visual mechanisms,image enhancement,image suppression,image regions,geometrical grouplets,complex low-level features,psychophysical chromatic induction data,state-of-the-art methods,Image color analysis,Wavelet transforms,Visualization,Measurement,Image representation,Biological system modeling,hierarchical image representation,Computational models of vision,color
N. Murray, M. Vanrell, X. Otazu, C. A. Parraga, "Low-Level Spatiochromatic Grouping for Saliency Estimation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 11, pp. 2810-2816, Nov. 2013, doi:10.1109/TPAMI.2013.108
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