Indoor Outdoor Scene Classification in Digital Images

In this paper, we present a method to classify real-world digital images into indoor and outdoor scenes. Indoor class consists of four groups: bedroom, kitchen, laboratory and library. Outdoor class consists of four groups: landscape, roads, buildings and garden. Application considers real-time system and has a dedicated data-set. Input images are pre-processed and converted into gray-scale and is re-sized to “128x128” dimensions. Pre-processed images are sent to “Gabor filters”, which pre-computes filter transfer functions, which are performed on Fourier domain. The processed signal is finally sent to GIST feature extraction and the images are classified using “kNN classifier”. Most of the techniques have been based on the use of texture and color space features. As of date, we have been able to achieve 80% accuracy with respect to image classification