Anomaly Detection in Natural Scene Images Based on Enhanced Fine-Grained Saliency and Fuzzy Logic

Mokayed, Hamam; Shivakumara, Palaiahnakote; Saini, Rajkumar; Liwicki, Marcus; Chee Hin, Loo; Pal, Umapada

Abstract

This paper proposes a simple yet effective method for anomaly detection in natural scene images improving natural scene text detection and recognition. In the last decade, there has been significant progress towards text detection and recognition in natural scene images. However, in cases where there are logos, company symbols, or other decorative elements for text, existing methods do not perform well. This work considers such misclassified components, which are part of the text as anomalies, and presents a new idea for detecting such anomalies in the text for improving text detection and recognition in natural scene images. The proposed method considers the result of the existing text detection method as input for segmenting characters or components based on saliency map and rough set theory. For each segmented component, the proposed method extracts feature from the saliency map based on density, pixel distribution, and phase congruency to classify text and non-text components by exploring a fuzzy-based classifier. To verify the effectiveness of the method, we have performed experiments on several benchmark datasets of natural scene text detection, namely, MSRATD-500 and SVT. Experimental results show the efficacy of the proposed method over the existing ones for text detection and recognition in these datasets.

Citation

Mokayed, H., Shivakumara, P., Saini, R., Liwicki, M., Chee Hin, L., & Pal, U. (2021). Anomaly Detection in Natural Scene Images Based on Enhanced Fine-Grained Saliency and Fuzzy Logic. IEEE Access, 9, https://doi.org/10.1109/ACCESS.2021.3103279

• Journal Article Type: Article
• Acceptance Date: Jul 15, 2021
• Publication Date: Aug 9, 2021
• Deposit Date: Feb 2, 2024
• Publicly Available Date: Feb 5, 2024
• Journal: IEEE Access
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 9
• DOI: https://doi.org/10.1109/ACCESS.2021.3103279