Fine-grained object classification based on block diagonal feature and ensemble learning

Xue Xie; Beiyi Luan; Shiqing Song; Jilin Wang

doi:10.1117/12.2623246

16 February 2022 Fine-grained object classification based on block diagonal feature and ensemble learning

Xue Xie, Beiyi Luan, Shiqing Song, Jilin Wang

Proceedings Volume 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021); 120831B (2022) https://doi.org/10.1117/12.2623246
Event: Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 2021, Kunming, China

Abstract

Image classification is an important research branch in the field of computer vision. Ordinary image classification focuses on the discrimination of object differences, while fine-grained image classification focuses on the distinction between different sub-categories. Considering the small differences between sub-categories and the larger differences within sub-categories due to factors such as shooting angle, background, and posture, fine-grained image recognition becomes a challenging task. In order to solve the problem of fine-grained object classification, this paper proposes a finegrained object classification method based on block diagonal feature and ensemble learning. The Gabor feature of the object image is block diagonalized by using low-rank recovery technology, and the low-rank feature representation of the object is constructed by introducing the block diagonal sparse regular term to increase the discriminability of features between object sub-categories. On this basis, the stacking ensemble learning method is used to classify objects, and a fine-grained real animal image and non-real animal image (toy animal image) classification database is constructed. And the algorithm design and experiment are completed. Through the proposed algorithm, accuracy of fine-grained classification of objects is improved

Citation Download Citation

Xue Xie, Beiyi Luan, Shiqing Song, and Jilin Wang "Fine-grained object classification based on block diagonal feature and ensemble learning", Proc. SPIE 12083, Thirteenth International Conference on Graphics and Image Processing (ICGIP 2021), 120831B (16 February 2022); https://doi.org/10.1117/12.2623246

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KEYWORDS

Image classification

Databases

Feature extraction

Detection and tracking algorithms

Associative arrays

Classification systems

Image processing

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