Locally Important Pattern Clustering Code for Pedestrian Classification

Young Chul LIM; Minsung KANG

doi:10.1587/transfun.E98.A.1875

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Young Chul LIM, Minsung KANG, "Locally Important Pattern Clustering Code for Pedestrian Classification" in IEICE TRANSACTIONS on Fundamentals, vol. E98-A, no. 8, pp. 1875-1878, August 2015, doi: 10.1587/transfun.E98.A.1875.
Abstract: In this letter, a local pattern coding scheme is proposed to reduce the dimensionality of feature vectors in the local ternary pattern. The proposed method encodes the ternary patterns into a binary pattern by clustering similar ternary patterns. The experimental results show that the proposed method outperforms the previous methods.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/transfun.E98.A.1875/_p

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@ARTICLE{e98-a_8_1875,
author={Young Chul LIM, Minsung KANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Locally Important Pattern Clustering Code for Pedestrian Classification},
year={2015},
volume={E98-A},
number={8},
pages={1875-1878},
abstract={In this letter, a local pattern coding scheme is proposed to reduce the dimensionality of feature vectors in the local ternary pattern. The proposed method encodes the ternary patterns into a binary pattern by clustering similar ternary patterns. The experimental results show that the proposed method outperforms the previous methods.},
keywords={},
doi={10.1587/transfun.E98.A.1875},
ISSN={1745-1337},
month={August},}

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TY - JOUR
TI - Locally Important Pattern Clustering Code for Pedestrian Classification
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1875
EP - 1878
AU - Young Chul LIM
AU - Minsung KANG
PY - 2015
DO - 10.1587/transfun.E98.A.1875
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E98-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2015
AB - In this letter, a local pattern coding scheme is proposed to reduce the dimensionality of feature vectors in the local ternary pattern. The proposed method encodes the ternary patterns into a binary pattern by clustering similar ternary patterns. The experimental results show that the proposed method outperforms the previous methods.
ER -