Visualization of confusion matrices with network graphs

Gilbraith, William E.; Celani, Caelin P.; Booksh, Karl S.

doi:10.1002/cem.3435

Cited by 3 publications

(1 citation statement)

References 39 publications

(52 reference statements)

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“…Finally, we confirm the enhancement in class-specific accuracy achieved by the geological borehole image recognition database. To compare the results of the two databases in the aforementioned classification models, we employed a widely used indicator in statistical classification problems, namely the confusion matrix [44]. Figure 19 shows the confusion matrix comparisons for the border images (Bi), fracture images (Fi), and intact rock images (Irmi).…”

Section: Analysis Of Image Recognition Performancementioning

confidence: 99%

Advancing Borehole Imaging: A Classification Database Developed via Adaptive Ring Segmentation

Deng,

Han,

Liu

et al. 2024

Electronics

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The use of in-hole imaging to investigate geological structure characteristics is one of the crucial methods for the study of rock mass stability and rock engineering design. The in-hole images are usually influenced by the lighting and imaging characteristics, resulting in the presence of interference noise regions in the images and consequently impacting the classification accuracy. To enhance the analytical efficacy of in-hole images, this paper employs the proposed optimal non-concentric ring segmentation method to establish a new database. This method establishes the transformation function based on the Ansel Adams Zone System and the fluctuation values of the grayscale mean, adjusting the gray-level distribution of images to extract two visual blind spots of different scales. Thus, the inner and outer circles are located with these blind spots to achieve the adaptive acquisition of the optimal ring. Finally, we use the optimal non-concentric ring segmentation method to traverse all original images to obtain the borehole image classification database. To validate the effectiveness of this method, we conduct experiments using various segmentation and classification evaluation metrics. The results show that the Jaccard and Dice of the optimal non-concentric ring segmentation approach are 88.43% and 98.55%, respectively, indicating superior segmentation performance compared to other methods. Furthermore, after employing four commonly used classification models to validate the performance of the new classification database, the results demonstrate a significant improvement in accuracy and macro-average compared to the original database, with the highest increase in accuracy reaching 4.2%. These results fully demonstrate the effectiveness of the proposed optimal non-concentric ring segmentation method.

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Section: Analysis Of Image Recognition Performancementioning

confidence: 99%