Recommending the most confusing images to the annotators via confusion graph for the large-scale face dataset annotation

Lei Zhao; Peng Qiao; Yong Dou

doi:10.1117/12.2557953

3 January 2020 Recommending the most confusing images to the annotators via confusion graph for the large-scale face dataset annotation

Lei Zhao, Peng Qiao, Yong Dou

Author Affiliations +

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 1137304 (2020) https://doi.org/10.1117/12.2557953
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

Abstract

Image set annotation is an important task in the supervised training of the deep neural network. Manual and data-driven dataset annotation methods are commonly used approaches. Both of them have shortcomings, especially in the case of the dataset requiring professional knowledge, which leads to high cost with manual annotation methods and poorly diversified annotation samples with data-driven annotation methods. Although the recommendation annotation method based on cosine similarity using deep neural network features takes advantages of manual annotation and data-driven method, there are still problems such as low accuracy and click-through rate. In order to improve the recommendation accuracy and click-through rate, we propose a confusion graph recommendation annotation method, which builds a confusion graph based on the Largest Margin Nearest Neighbor (LMNN) distance among deep neural network features, to recommend the most confusing images to annotators. In this paper, we made ablation studies on the self-built child face dataset in terms of Precision, mAP (mean Average Precision), and CTR (click-through-rate). The experimental results show that the proposed method achieves superior performance, compared with the cosine similarity recommendation annotation method and the manual annotation method.

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Lei Zhao, Peng Qiao, and Yong Dou "Recommending the most confusing images to the annotators via confusion graph for the large-scale face dataset annotation", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 1137304 (3 January 2020); https://doi.org/10.1117/12.2557953

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KEYWORDS

Image processing

Detection and tracking algorithms

Facial recognition systems

Neural networks

Feature extraction

Visualization

Data modeling

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