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Compressed SIFT Feature Based Matching

Authors:
Shmuel Tomi Klein
Dana Shapira

Keywords: Data Compression; Feature vectors; SIFT; Fibonacci code

Abstract:
The problem of compressing a large collection of feature vectors so that object identification can further be processed on the compressed form of the features is investigated. The idea is to perform matching against a query image in the compressed form of the descriptor vectors retaining the metric. Specifically, we concentrate on the Scale Invariant Feature Transform (SIFT), a known object detection method. Given two SIFT feature vectors, we suggest achieving our goal by compressing them using a lossless encoding for which the pairwise matching can be done directly on the compressed files, by means of a Fibonacci code. Experiments show that this approach incurs only a small loss in compression efficiency relative to standard compressors requiring a decoding phase.

Pages: 64 to 69

Copyright: Copyright (c) IARIA, 2014

Publication date: July 20, 2014

Published in: conference

ISSN: 2326-9332

ISBN: 978-1-61208-364-3

Location: Paris, France

Dates: from July 20, 2014 to July 24, 2014