A multi-descriptor, multi-nearest neighbor approach for image classification

Authors:
Dongjian He;Shangsong Liang;Yong Fang
Affiliations:
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, ShaanXi, China;College of Information Engineering, Northwest A&F University, Yangling, ShaanXi, China;College of Information Engineering, Northwest A&F University, Yangling, ShaanXi, China
Venue:
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Year:
2010

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Abstract

Three practices commonly used in image classification methods have led to the inferior performance of Nearest-Neighbor (NN) based image classifiers: (i)Way of constructing "bag-of-visual-words". (ii)Using only one kind of descriptor. (iii)Without considering which category the NN comes from. We propose a novel NN-based classifier -MDMNN (Multi-Descriptor Multi-Nearest Neighbor), which classifies an unlabeled image by employing its nearest neighbors coming from all of the categories and different kinds of feature descriptors to images. Empirically, we conduct experiments on two real-world image databases, and show that although MDMNN requires no learning phase, its performance ranks some outstanding learning-based image classifiers.