An Image Retrieval Method Based on Manifold Learning with Scale-Invariant Feature Control
Abstract: Aiming at the
problem of the traditional dimensionality reduction methods cannot recover the inherent
structure, and scale invariant feature transform (SIFT) achieving low precision
when reinstatingimages, an Image Retrieval Method Based on Manifold Learning
with Scale-Invariant Feature is proposed. It aims to find low-dimensional
compact representations of high-dimensional observation data and explores the
inherent low and intrinsic dimension of data. The feature extraction
method-SIFT and the adaptive ISOMAP method are combined and conducted
experiments on the ORL face image dataset. This paper analyzes and discusses
the problem of effects of the neighborhood parameter and the intrinsic
dimension size on the face image recognition.
Author: Haifeng Guo
Journal Code: jptkomputergg160063
