Hybrid Relative Attributes Based on Sparse Coding for Zero-Shot Image Classification
Joint Authors
Yun, Xiao
Lu, Nannan
Sun, Yan-jing
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-21
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
As a specific case of image recognition, zero-shot image classification is difficult to solve since its training set cannot cover all the categories of the testing set.
From the view point of human vision recognition, the objects can be recognized through the visible and nameable description to the properties.
To be the semantic description of the object property, attributes can be taken as a bridge between the seen and unseen categories, which are capable of using into zero-shot image classification.
There are mainly binary attributes and relative attributes for zero-shot classification, where the relative attributes have the ability to catch more general sematic relationship than the binary ones.
But relative attributes do not always work in zero-shot classification for those categories having similar relative strength attributes.
Aiming at solving the defect of the relative attributes in describing the similar categories, we propose to construct the Hybrid Relative Attributes based on Sparse Coding (SC-HRA).
First, sparse coding is implemented on low-level features to get nonsemantic relative attributes, which are the necessary complement to the existing relative attributes.
After that, they are integrated with the relative attributes to form the hybrid relative attributes (HRA).
HRA ranking functions are then learned by the relative attribute learning.
Finally, the class label is obtained according to the predicted ranking results of HRA and the ranking relations of HRA among the categories.
To verify the effectiveness of SC-HRA, the extensive experiments are conducted on the datasets of faces and natural scenes.
The results show that SC-HRA acquires the higher classification accuracy and AUC value.
American Psychological Association (APA)
Lu, Nannan& Sun, Yan-jing& Yun, Xiao. 2019. Hybrid Relative Attributes Based on Sparse Coding for Zero-Shot Image Classification. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1196978
Modern Language Association (MLA)
Lu, Nannan…[et al.]. Hybrid Relative Attributes Based on Sparse Coding for Zero-Shot Image Classification. Mathematical Problems in Engineering No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1196978
American Medical Association (AMA)
Lu, Nannan& Sun, Yan-jing& Yun, Xiao. Hybrid Relative Attributes Based on Sparse Coding for Zero-Shot Image Classification. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1196978
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1196978