Scene classification with a sparse set of salient regions

This work proposes an approach for scene classification by extracting and matching visual features only at the focuses of visual attention instead of the entire scene. Analysis over a database of natural scenes demonstrates that regions proposed by the saliency-based model of visual attention are robust to image transformations. Using a nearest neighbor classifier and a distance measure defined over the salient regions, we obtained 97.35% and 78.28% classification rates with SIFT and C2 features from the HMAX model at 5 salient regions covering at most 31% of the image. Classification with features extracted from the entire image results in 99.3% and 82.32% using SIFT and C2 features, respectively. Comparing attentional and adhoc approaches shows that classification rate of the first approach is 0.95 of the second. Overall, our results prove that efficient scene classification, in terms of reducing the complexity of feature extraction is possible without a significant drop in performance.