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BAMBOO: A FAST DESCRIPTOR BASED ON ASYMMETRIC PAIRWISE BOOSTING

Articles

Journal/Conference:

2014 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)

Language:

English

Author:

Baroffio Luca; Cesana Matteo; Redondi Alessandro; Tagliasacchi Marco

Year:

2014

Pages:

5686-5690

Keywords:

Binary descriptors; robust hash; digital fingerprinting; boosting

A robust hash, or content-based fingerprint, is a succinct representation of the perceptually most relevant parts of a multimedia object. A key requirement of fingerprinting is that elements with perceptually similar content should map to the same fingerprint, even if their bit-level representations are different. In this work we propose BAMBOO (Binary descriptor based on AsymMetric pairwise BOOsting), a binary local descriptor that exploits a combination of content-based fingerprinting techniques and computationally efficient filters (box filters, Haar-like features, etc.) applied to image patches. In particular, we define a possibly large set of filters and iteratively select the most discriminative ones resorting to an asymmetric pairwise boosting technique. The output values of the filtering process are quantized to one bit, leading to a very compact binary descriptor. Results show that such descriptor leads to compelling results, significantly outperforming binary descriptors having comparable complexity (e.g., BRISK), and approaching the discriminative power of state-of-the-art descriptors which are significantly more complex (e.g., SIFT and BinBoost).