Paper Info
Title
Structure preserving dimensionality reduction for visual object recognition.
Abstract
Robust object recognition has drawn increasing attention in the field of computer vision and machine learning with fast development in feature extraction and classification techniques, and release of public datasets, such as Caltech datasets, Pascal Visual Object Classes, and ImageNet. Recently, deep learning based object recognition systems have shown significant performance improvements in visual object recognition tasks using innovative learning methodology. However, high dimensional space searching and recognition is time consuming, so performing point and range queries in high dimension is reconsidered for object recognition. This paper proposes optimized dimensionality reduction using structured sparse principle component analysis. The proposed method retains high dimensional feature structures, removes redundant features that do not contribute to similarity, and classifies the query image in a large database. The qualitative and quantitative experimental results, including a comparison with the current state-of-the-art visual object recognition algorithms, verify that the proposed recognition algorithm performs favorably in reducing the query image dimension and number of training images.
Year
DOI
Venue
2018
10.1007/s11042-018-5682-5
Multimedia Tools Appl.
Keywords
Field
DocType
Dimensionality reduction, Object recognition, Structured sparse PCA
Computer vision,Dimensionality reduction,Pattern recognition,Computer science,Range query (data structures),Feature extraction,Artificial intelligence,High dimensional space,Recognition algorithm,Deep learning,Principal component analysis,Cognitive neuroscience of visual object recognition
Journal
Volume
Issue
ISSN
77
18
1380-7501
Citations 
PageRank 
References 
2
0.36
28
Authors
4
Name
Order
Citations
PageRank
jinjoo song173.80
Gang-Joon Yoon2327.66
Heeryon Cho3709.38
Sang Min Yoon412919.66