Feature tracking for wide-baseline image retrieval

We address the problem of large scale image retrieval in a wide-baseline setting, where for any query image all the matching database images will come from very different viewpoints. In such settings traditional bag-of-visual-words approaches are not equipped to handle the significant feature descriptor transformations that occur under large camera motions. In this paper we present a novel approach that includes an offline step of feature matching which allows us to observe how local descriptors transform under large camera motions. These observations are encoded in a graph in the quantized feature space. This graph can be used directly within a soft-assignment feature quantization scheme for image retrieval. Key words: Wide baseline, image retrieval, quantization

• Type: Conference
• Year: 2010
• Where: ECCV
• Keywords: Computer Vision, ECCV 2010, Image Retrieval, Large Camera Motions, Scale Image Retrieval