| Summary: | 碩士 === 國立中正大學 === 資訊工程研究所 === 100 === Scene categorization acts as an essential part in many applications since scene type of an image provides abundant information for media analysis. Most works about scene categorization target on gray images, and rely on oriented gradient calculated based on intensity values as local descriptors. With these descriptors, the bag of word model is used in describing scene images. However, a visual word may be generated from different objects in various categories, and discriminative capability of visual words may hence decrease. On the other hand, exhaustive computation makes processes inefficient. In this thesis, we propose a fast scene categorization system to solve the problems mentioned above.
We would like to study scene categorization for color images. We devise a new visual descriptor that incorporates color information into the framework of CENsus TRansform hISTogram (CENTRIST), a state-of-the-art visual descriptor for scene categorization. CENTRIST mainly encodes the structural properties within an image and suppresses detailed textural information. It is suitable to place and scene recognition task. Based on CENTRIST, we devise a new visual descriptor, i.e., color CENTRIST, that incorporates the advantage of CENTRIST and color information. The newly proposed color CENTRIST descriptor describes global shape information by not only gradient derived from intensity values but also color variations between pixels in local image patches. With color information, scenes of images can be effectively categorized.
Through extensive evaluations on various datasets, we demonstrate that the color CENTRIST descriptor is not only easily to be implemented, but also reliably achieves performance over that of CENTRIST. Considering color information indeed benefits scene categorization.
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