1. A hybridized modified densenet deep architecture with CLAHE algorithm for humpback whale identification and recognition.
- Author
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Vidyarthi, Ankit and Malik, Aruna
- Subjects
HUMPBACK whale ,MULTIMEDIA systems ,INFORMATION storage & retrieval systems ,BIOMETRIC identification ,ALGORITHMS ,DEEP learning - Abstract
Authenticity in multimedia information retrieval system is one such parameter which is of utmost in demand. One of such parameters is the biometric based authentication system, as seen in case of human the system proves its importance in almost each and every field. But in case of animals it becomes quite complex to recognize them using unique biometric parameter. This paper presents a modified 10 layered DenseNet deep learning framework which is implemented for the identification of Humpback whale, in an online challenge conducted by the kaggle, using biometric parameter. The proposed dense model minimizes the traditional limitation of vanishing gradient problem and outperforms as compared with other methods exists in the literature. This paper also proposed an hand free flow algorithm for ROI segmentation. In addition, the proposed methodology also uses CLAHE as a preprocessing method and focused on the feature re-usability with an optimized number of parameters used for recognition. During the experimentation, validation of the proposed model is found satisfactory with an accuracy of 93.21%. The test set results are provided by the kaggle with an AUC-ROC result over public and private scoreboard with an accuracy of 94.47% and 92.54% respectively. The comparative result with other deep methodologies suggests that the highest accuracy gained is 95.55% by Se-ResNet50 by some other learderboard and proposed model is found at second place. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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