Scale-space multi-view bag of words for scene categorization

As a widely-used method in the image categorization tasks, the Bag-of-Words (BoW) method still suffers from many limitations such as overlooking spatial information. In this paper, we propose four improvements to the BoW method to consider spatial and semantic information as well as information from multiple views. In particular, our contributions are: (a) encoding spatial information based on a combination of wavelet transform image scaling and a new image partitioning scheme, (b) proposing a spatial-information- and content-aware visual word dictionary generation approach, (c) developing a content-aware feature weighting approach to considers the significance of the features for different semantics, (d) proposing a novel weighting strategy to fuse color information when discriminative shape features are lacking. We call our method Scale-Space Multi-View Bag of Words (SSMV-BoW). We conducted extensive experiments to evaluate our SSMV-BoW and compare it to the state-of-the-art scene categorization methods. For our experiments, we use four publicly available and widely used scene categorization benchmark datasets. Results demonstrate that our SSMV-BoW outperforms the methods using both hand-crafted and deep learning features. In addition, ablation studies show that all four improvements contribute to the performance of our SSMV-BoW.

Automated summary

The study introduced a Scale-Space Multi-View Bag of Words (SSMV-BoW) method to address common limitations in image categorization tasks by considering spatial and semantic information as well as information from multiple views. Experimental results demonstrate the superior performance of this method compared to others.