A scene is a view of a real-world environment that contains multiples surfaces and objects, organized in a meaningful way. The Aim of Scene recognition system is understand Scene type that shown in the image and then assign appropriate semantic tags to it. Creating such a system is one of the most important issues in the field of computer vision and robotics and has many applications in object recognition, semantic image and video retrieval, motion detection, locating and guiding a robot. Scene can be divided into two categories: Indoor and outdoor. Indoor scene includes enviroments such as kitchens, hospitals and even within the vehicle and the outdoor scene, including open spaces, such as streets, beach and mountains. There are some previous works devoted to the task of scene recognition, but they usually only perform well on outdoors. In contrast, recognition indoor images have remained a further challenging task due to the more difficult nature of the problem. The intra- kashida; TEXT-ALIGN: justify; LINE-HEIGHT: 90%; TEXT-KASHIDA: 0%; MARGIN: 12pt 0cm 10pt; unicode-bidi: embed; DIRECTION: ltr" In this thesis, we are designe a system to recognize indoor images. The algorithm contains two basic steps: extract data of images and scene modeling using this data. To extract local features of image are used set of SIFT, HOG and LBP descriptors. Also, to extract information on the spatial relationships in image regions are calculated Co-occurrence matrixs in several angels. Then, we use this information to semantic image segmentation and graph cut energy minimization to image labeling. The proposed method is invariant toward changes of scale, rotation, transformation, contrast, view point and occlusion and clutter in images. Another important feature of the proposed method is the ability to generalize to test images. Keywords: Computer vision, Scene Recognition, Semantic Segmentation, Local Semantic Concepts.
