Collaborative hashing adopted in locality-constrained linear coding for scene classification

Scene classification methods based on effective feature extraction and coding have obtained promising results in recent years. But the K-nearest neighbor search strategy in Locality-constrained Linear Coding (LLC) increases the complexity of the algorithm due to the exhaustive search. To solve the p...

Full description

Saved in:
Bibliographic Details
Published in:Multimedia tools and applications Vol. 78; no. 12; pp. 16329 - 16343
Main Authors: Zhou, Zhiping, Li, Chunye, Zhao, Xiaoxiao, Zhou, Fangzheng
Format: Journal Article
Language:English
Published: New York Springer US 01.06.2019
Springer Nature B.V
Subjects:
Binary codes
Classification
Coding
Collaboration
Computer Communication Networks
Computer Science
Computing time
Data Structures and Information Theory
Euclidean geometry
Euclidean space
Feature extraction
Mapping
Multimedia Information Systems
Search methods
Special Purpose and Application-Based Systems
Approximate nearest neighbor
Collaborative hashing
Locality-constrained linear coding
ISSN:1380-7501, 1573-7721
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Scene classification methods based on effective feature extraction and coding have obtained promising results in recent years. But the K-nearest neighbor search strategy in Locality-constrained Linear Coding (LLC) increases the complexity of the algorithm due to the exhaustive search. To solve the problem, an improved approximate nearest neighbor search strategy is proposed to improve the computational efficiency of LLC. Considering the mapping relationship between the visual words and features, a collaborative hashing method is incorporated to transform the high dimensional features into binary code form, and the original Euclidean space is transformed into the Hamming space that consists of multi similar features. The similar visual words can be queried quickly. Then the nearest neighbors can be searched efficiently through Hamming distance ranking, which can improve the coding efficiency. The experimental results on standard datasets demonstrate the effectiveness of the proposed approach, and the average classification accuracy can be improved.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-018-6978-1