Binary Co-occurrences of Weak Descriptors
Author(s) -
Martin Winter,
Horst Bischof
Publication year - 2007
Publication title -
citeseer x (the pennsylvania state university)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5244/c.21.13
Subject(s) - scale invariant feature transform , discriminative model , pattern recognition (psychology) , artificial intelligence , computer science , binary number , joint (building) , cognitive neuroscience of visual object recognition , quantization (signal processing) , representation (politics) , scaling , local binary patterns , object (grammar) , feature extraction , histogram , computer vision , mathematics , image (mathematics) , arithmetic , architectural engineering , geometry , politics , law , political science , engineering
This paper demonstrates that a reliable and efficient object recognition system based only on binary joint occurrences of quantized descriptors can be built. Specifically, we show that a high recognition performance can be obtained even with very weak (non discriminative) descriptors. The binary joint occurrence representation despite being high dimensional is very sparse and therefore efficient. In order to obtain reliable joint occurrences we present a fast hierarchical quantization algorithm. We illustrate our results using different descriptors (PCA-SIFT, Spin images, SIFT) on a challenging, specific object recognition task and consider the scaling behavior of the method.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom