Is Bottom-Up Attention Useful for Scene Recognition?

TL;DR

This paper explores how bottom-up attention mechanisms, like saliency, can enhance scene recognition by improving accuracy and computational efficiency, especially with limited training data.

Contribution

It introduces a novel method combining salient and non-salient regions via Multiple Kernel Learning for better scene classification.

Findings

Pruning achieves high computational savings with minimal accuracy loss.

Saliency weighting alone does not improve classification performance.

The proposed method outperforms baseline approaches on the UIUC sports dataset with small training sets.

Abstract

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.