Fine-Grained Object Recognition Using a Combination Model of Navigator–Teacher–Scrutinizer and Spinal Networks

Fine-grained object recognition aims to recognize objects with a large variety of intraclass and low variations between classes. To overcome this problem, using a simple model may hard to find more discriminative parts. Thus, we proposed a combination model of navigator–teacher–scrutinizer and spina...

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Bibliographic Details
Published inPattern recognition and image analysis Vol. 33; no. 1; pp. 47 - 53
Main Authors Nurhasanah, Yulianto, Kusuma, Gede Putra
Format Journal Article
LanguageEnglish
Published Moscow Pleiades Publishing 01.03.2023
Springer Nature B.V
Subjects
Applied Problems
Computer Science
Feature extraction
Image Processing and Computer Vision
Networks
Object recognition
Pattern Recognition
Teachers
residual network
spinal network
navigator–teacher–scrutinizer network
fine-grained recognition
combined model
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Summary:Fine-grained object recognition aims to recognize objects with a large variety of intraclass and low variations between classes. To overcome this problem, using a simple model may hard to find more discriminative parts. Thus, we proposed a combination model of navigator–teacher–scrutinizer and spinal networks to improve accuracy. Employing two feature extractors, residual networks with 50 and 101 layers deep, and replacing the basic fully connected layer with spinal network outperform the baseline results on Stanford Cars, Fine-Grained Visual Classification of Aircraft, and 275 Bird Species datasets.
ISSN:1054-6618
1555-6212
DOI:10.1134/S1054661822040083