A method was developed to detect generic objects using a single query image. The query image could be a typical real image, a virtual image, or even a hand-drawn sketch of the object. Without a training process, the k...A method was developed to detect generic objects using a single query image. The query image could be a typical real image, a virtual image, or even a hand-drawn sketch of the object. Without a training process, the key problem is how to describe the object class from only one query image with no pre-segmentation or other pre-processing procedures. The method introduces densely computed Scale-lnvariant Feature Transform （SIFT） as the descriptor to extract ＂gradient distribution＂ features of the image. The descriptor emphasizes the edge parts and their distribution structures, which are very representative of the object class, so it is very robust and can deal with virtual images or hand-drawn sketches. Tests on car detection, face detection, and generic object detection demonstrate that the method is effective, robust, and widely applicable. The results using queries of real images compare well with other training-free methods and state-of-the-art training-based methods.展开更多
基金Supported by the National Key Basic Research and Development (973) Program of China (No.2007CB311004)
文摘A method was developed to detect generic objects using a single query image. The query image could be a typical real image, a virtual image, or even a hand-drawn sketch of the object. Without a training process, the key problem is how to describe the object class from only one query image with no pre-segmentation or other pre-processing procedures. The method introduces densely computed Scale-lnvariant Feature Transform （SIFT） as the descriptor to extract ＂gradient distribution＂ features of the image. The descriptor emphasizes the edge parts and their distribution structures, which are very representative of the object class, so it is very robust and can deal with virtual images or hand-drawn sketches. Tests on car detection, face detection, and generic object detection demonstrate that the method is effective, robust, and widely applicable. The results using queries of real images compare well with other training-free methods and state-of-the-art training-based methods.
