Classification of indoor/outdoor scene

Scene classification is essential in Content Based Image Retrieval system which processes large amount of data. But this retrieval system failed to produce good classification result due to less relevant visual features used for classification task. In this work, we aim to categorize scenes into indoor versus outdoor using relevant low level features such as color and texture which help to improve the classification performance. The proposed method uses statistical feature computed from HSV color model as color feature, DCT coefficients as texture feature and entropy computed with UV. A simple non-parametric K-nearest neighbor classifier is used in conjunction with this low level features to categorize scene into indoor versus outdoor. Since these image features exhibit a distinctive disparity between images containing indoor or outdoor scenes the proposed method achieves better performance in terms of classification accuracy about 81.5% when less number training images are used. The proposed method is evaluated on IITM-SCID 2 (scene classification image database) as well as 2011 images collected from the web.