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

Jienan ZHANG  Shouyi YIN  Peng OUYANG  Leibo LIU  Shaojun WEI  

Publication
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E96-A   No.6   pp.1357-1365
Publication Date: 2013/06/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E96.A.1357
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on Circuit, System, and Computer Technologies)
Category: 
Keyword: 
p-SIFT,  individual object,  object detection,  object recognition,  

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Summary: 
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.