Learning Binary Codes for High Dimensional Data Using Bilinear Projections
Venue
IEEE Computer Vision and Pattern Recognition (2013)
Publication Year
2013
Authors
Yunchao Gong, Sanjiv Kumar, Henry Rowley, Svetlana Lazebnik
BibTeX
Abstract
Recent advances in visual recognition indicate that to achieve good retrieval and
classification accuracy on large scale datasets like ImageNet, extremely
high-dimensional visual descriptors, e.g., Fisher Vectors, are needed. We present a
novel method for converting such descriptors to compact similarity-preserving
binary codes that exploits their natural matrix structure to reduce their
dimensionality using compact bilinear projections instead of a single large
projection matrix. This method achieves comparable retrieval and classification
accuracy to the original descriptors and to the state-of-the-art Product
Quantization approach while having orders of magnitude faster code generation time
and smaller memory footprint.
