Abstract
We propose a content-based solar image descriptor for fast retrieving similar images. The method is divided into three main stages: active region detection by using edge detection, representation learning and hash generation. The first step uses morphological operations for active region detection and afterwards Canny edge detection. In the learning step we use an unsupervised convolutional autoencoder in order to obtain the solar image hash. This process reduces hash length more than twelve times compared to the active region image matrix. The process of reducing the hash length is significant in reference to solar image retrieval process, in which we focus on calculating the distances between hashes. The performed experiments proved the efficiency of the proposed approach. The presented method has various potential, not only solar, applications. Moreover, the problem of searching of and retrieving solar flares has a significant impact on many aspects of life on Earth and beyond.
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Grycuk, R., Najgebauer, P., Scherer, R. (2023). Edge Detection-Based Full-Disc Solar Image Hashing. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2022. Lecture Notes in Computer Science(), vol 13589. Springer, Cham. https://doi.org/10.1007/978-3-031-23480-4_20
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