Concurrent Detection and Recognition of Individual Object Based on Colour and p-SIFT Features

Jienan ZHANG; Shouyi YIN; Peng OUYANG; Leibo LIU; Shaojun WEI

doi:10.1587/transfun.E96.A.1357

Concurrent Detection and Recognition of Individual Object Based on Colour and p-SIFT Features

Jienan ZHANG, Shouyi YIN, Peng OUYANG, Leibo LIU, Shaojun WEI

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In this paper we propose a method to use features of an individual object to locate and recognize this object concurrently in a static image with Multi-feature fusion based on multiple objects sample library. This method is proposed based on the observation that lots of previous works focuses on category recognition and takes advantage of common characters of special category to detect the existence of it. However, these algorithms cease to be effective if we search existence of individual objects instead of categories in complex background. To solve this problem, we abandon the concept of category and propose an effective way to use directly features of an individual object as clues to detection and recognition. In our system, we import multi-feature fusion method based on colour histogram and prominent SIFT (p-SIFT) feature to improve detection and recognition accuracy rate. p-SIFT feature is an improved SIFT feature acquired by further feature extraction of correlation information based on Feature Matrix aiming at low computation complexity with good matching rate that is proposed by ourselves. In process of detecting object, we abandon conventional methods and instead take full use of multi-feature to start with a simple but effective way-using colour feature to reduce amounts of patches of interest (POI). Our method is evaluated on several publicly available datasets including Pascal VOC 2005 dataset, Objects101 and datasets provided by Achanta et al.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E96-A No.6 pp.1357-1365

Publication Date: 2013/06/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1587/transfun.E96.A.1357

Type of Manuscript: Special Section PAPER (Special Section on Circuit, System, and Computer Technologies)

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Authors

Jienan ZHANG
  Tsinghua University
Shouyi YIN
  Tsinghua University
Peng OUYANG
  Tsinghua University
Leibo LIU
  Tsinghua University
Shaojun WEI
  Tsinghua University

Keyword

p-SIFT, individual object, object detection, object recognition

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Jienan ZHANG, Shouyi YIN, Peng OUYANG, Leibo LIU, Shaojun WEI, "Concurrent Detection and Recognition of Individual Object Based on Colour and p-SIFT Features" in IEICE TRANSACTIONS on Fundamentals, vol. E96-A, no. 6, pp. 1357-1365, June 2013, doi: 10.1587/transfun.E96.A.1357.
Abstract: In this paper we propose a method to use features of an individual object to locate and recognize this object concurrently in a static image with Multi-feature fusion based on multiple objects sample library. This method is proposed based on the observation that lots of previous works focuses on category recognition and takes advantage of common characters of special category to detect the existence of it. However, these algorithms cease to be effective if we search existence of individual objects instead of categories in complex background. To solve this problem, we abandon the concept of category and propose an effective way to use directly features of an individual object as clues to detection and recognition. In our system, we import multi-feature fusion method based on colour histogram and prominent SIFT (p-SIFT) feature to improve detection and recognition accuracy rate. p-SIFT feature is an improved SIFT feature acquired by further feature extraction of correlation information based on Feature Matrix aiming at low computation complexity with good matching rate that is proposed by ourselves. In process of detecting object, we abandon conventional methods and instead take full use of multi-feature to start with a simple but effective way-using colour feature to reduce amounts of patches of interest (POI). Our method is evaluated on several publicly available datasets including Pascal VOC 2005 dataset, Objects101 and datasets provided by Achanta et al.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E96.A.1357/_p

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@ARTICLE{e96-a_6_1357,
author={Jienan ZHANG, Shouyi YIN, Peng OUYANG, Leibo LIU, Shaojun WEI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Concurrent Detection and Recognition of Individual Object Based on Colour and p-SIFT Features},
year={2013},
volume={E96-A},
number={6},
pages={1357-1365},
abstract={In this paper we propose a method to use features of an individual object to locate and recognize this object concurrently in a static image with Multi-feature fusion based on multiple objects sample library. This method is proposed based on the observation that lots of previous works focuses on category recognition and takes advantage of common characters of special category to detect the existence of it. However, these algorithms cease to be effective if we search existence of individual objects instead of categories in complex background. To solve this problem, we abandon the concept of category and propose an effective way to use directly features of an individual object as clues to detection and recognition. In our system, we import multi-feature fusion method based on colour histogram and prominent SIFT (p-SIFT) feature to improve detection and recognition accuracy rate. p-SIFT feature is an improved SIFT feature acquired by further feature extraction of correlation information based on Feature Matrix aiming at low computation complexity with good matching rate that is proposed by ourselves. In process of detecting object, we abandon conventional methods and instead take full use of multi-feature to start with a simple but effective way-using colour feature to reduce amounts of patches of interest (POI). Our method is evaluated on several publicly available datasets including Pascal VOC 2005 dataset, Objects101 and datasets provided by Achanta et al.},
keywords={},
doi={10.1587/transfun.E96.A.1357},
ISSN={1745-1337},
month={June},}

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TY - JOUR
TI - Concurrent Detection and Recognition of Individual Object Based on Colour and p-SIFT Features
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1357
EP - 1365
AU - Jienan ZHANG
AU - Shouyi YIN
AU - Peng OUYANG
AU - Leibo LIU
AU - Shaojun WEI
PY - 2013
DO - 10.1587/transfun.E96.A.1357
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E96-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2013
AB - In this paper we propose a method to use features of an individual object to locate and recognize this object concurrently in a static image with Multi-feature fusion based on multiple objects sample library. This method is proposed based on the observation that lots of previous works focuses on category recognition and takes advantage of common characters of special category to detect the existence of it. However, these algorithms cease to be effective if we search existence of individual objects instead of categories in complex background. To solve this problem, we abandon the concept of category and propose an effective way to use directly features of an individual object as clues to detection and recognition. In our system, we import multi-feature fusion method based on colour histogram and prominent SIFT (p-SIFT) feature to improve detection and recognition accuracy rate. p-SIFT feature is an improved SIFT feature acquired by further feature extraction of correlation information based on Feature Matrix aiming at low computation complexity with good matching rate that is proposed by ourselves. In process of detecting object, we abandon conventional methods and instead take full use of multi-feature to start with a simple but effective way-using colour feature to reduce amounts of patches of interest (POI). Our method is evaluated on several publicly available datasets including Pascal VOC 2005 dataset, Objects101 and datasets provided by Achanta et al.
ER -