Outdoor scene classification using invariant features

Scene classification using semantic description has gained much attention towards automatic image retrieval. In many cases, visual appearance of images get affected by environmental conditions such as low lighting and viewing conditions. Such problems in semantic scenes pose difficult challenges during the classification of sceneries. To address this issue, a new outdoor scene classification method for using low level feature has been proposed in this work. To support automatic scene classification at the concept level an efficient illumination and rotation invariant low level features such as color, texture and edge like features have been used in conjunction with multiclass Support Vector Machine (SVM). In this work, we have taken scene categories like mountains, forests, highways, rivers, buildings etc., from the outdoor scenes for classification experimentation. From the experimental results, we demonstrate that the proposed method provides better classification in the large scale image databases like Eight scene category, upright scene and COREL dataset and gives better performance in terms of classification accuracy.