Is Bottom-Up Attention Useful for Scene Recognition?
This work addresses scene recognition in computer vision, offering incremental improvements in efficiency and accuracy for applications like image analysis.
The paper tackled scene recognition by exploring computational models of human selective attention, comparing saliency weighting and pruning, and proposing a new method that separates and combines salient and non-salient regions using Multiple Kernel Learning. It found that pruning saves computation without sacrificing accuracy, while the new method improves classification accuracy over a baseline on the UIUC sports dataset with small training sizes.
The human visual system employs a selective attention mechanism to understand the visual world in an eficient manner. In this paper, we show how computational models of this mechanism can be exploited for the computer vision application of scene recognition. First, we consider saliency weighting and saliency pruning, and provide a comparison of the performance of different attention models in these approaches in terms of classification accuracy. Pruning can achieve a high degree of computational savings without significantly sacrificing classification accuracy. In saliency weighting, however, we found that classification performance does not improve. In addition, we present a new method to incorporate salient and non-salient regions for improved classification accuracy. We treat the salient and non-salient regions separately and combine them using Multiple Kernel Learning. We evaluate our approach using the UIUC sports dataset and find that with a small training size, our method improves upon the classification accuracy of the baseline bag of features approach.