Authors:
Ryuichi Ozaki
and
Kazunori Onoguchi
Affiliation:
Hirosaki University, Japan
Keyword(s):
Pedestrian detection, Co-occurrence Histograms of Oriented Gradients, Similarity, Support Vector Machine.
Related
Ontology
Subjects/Areas/Topics:
Feature Selection and Extraction
;
Pattern Recognition
;
Theory and Methods
Abstract:
Co-occurrence Histograms of Oriented Gradients(CoHOG) has succeeded in describing the detailed shape of
the object by using a co-occurrence of features. However, unlike HOG, it does not consider the difference of
gradient magnitude between the foreground and the background. In addition, the dimension of the CoHOG
feature is also very large. In this paper, we propose Similarity Co-occurrence Histogram of Oriented Gradients(
SCHOG) considering the similarity and co-occurrence of features. Unlike CoHOG which quantize edge
gradient direction to eight directions, SCHOG quantize it to four directions. Therefore, the feature dimension
for the co-occurrence between edge gradient direction decreases greatly. In addition to the co-occurrence
between edge gradient directions the binary code representing the similarity between features is introduced.
In this paper, we use the pixel intensity, the edge gradient magnitude and the edge gradient direction as the
similarity. In spite of redu
cing the resolution of the edge gradient direction, SCHOG realizes higher performance
and lower dimension than CoHOG by adding this similarity. We have focused on pedestrian detection
in this paper. However, this method is also applicable to various object recognition by introducing various
kind of similarity. In experiments using the INRIA Person Dataset, SCHOG is evaluated in comparison with
the conventional CoHOG.
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