Scene classification using a multi-resolution bag-of-features model

This paper presents a simple but effective scene classification approach based on the incorporation of a multi-resolution representation into a bag-of-features model. In the proposed approach, we construct multiple resolution images and extract local features from all the resolution images with dense regions. We then quantize these extracted features into a visual codebook using the k-means clustering method. To incorporate spatial information, two modalities of horizontal and vertical partitions are adopted to partition all resolution images into sub-regions with different scales. Each sub-region is then represented as a histogram of codeword occurrences by mapping the local features to the codebook. The proposed approach is evaluated over five commonly used data sets including indoor scenes, outdoor scenes, and sports events. The experimental results show that the proposed approach performs competitively against previous methods across all data sets. Furthermore, for the 8 scenes, 13 scenes, 67 indoor scenes, and 8 sport events data sets, the proposed approach outperforms state-of-the-art methods.

► We incorporate a multi-resolution representation into the bag-of-features model. ► The multi-resolution representation enables the approach to be more tolerant to image changes in scale. ► Two modalities of horizontal and vertical partitions are adopted to partition images into sub-regions. ► The multi-scale sub-region partition effectively exploits the spatial information of the image. ► For the four commonly used data sets, the proposed approach outperforms state-of-the-art methods.