Indoor-Outdoor Image Classification using Mid-Level Cues

Classifying an image into indoor/outdoor image category is very difficult due to vast range of variations in both of these scene categories. Most previous indoor-outdoor classification approaches utilize the simple statistics of the low-level features, such as colors, edges and textures. In this paper, we incorporate mid-level information to obtain superior scene description. We hypothesize that pixel based low-level descriptions are useful but can be improved with the introduction of mid-level region information. Experiments show that, while using mid-level features, it produces comparable result with that using low-level features. When combined with low-level features, the classification result get improved.