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. © 2010 Springer-Verlag.
CITATION STYLE
Makadia, A. (2010). Feature tracking for wide-baseline image retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6315 LNCS, pp. 310–323). Springer Verlag. https://doi.org/10.1007/978-3-642-15555-0_23
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