Semantic Hashing for Fast Solar Magnetogram Retrieval

Abstract

We propose a method for content-based retrieving solar magnetograms. We use the SDO Helioseismic and Magnetic Imager output collected with SunPy PyTorch libraries. We create a mathematical representation of the magnetic field regions of the Sun in the form of a vector. Thanks to this solution we can compare short vectors instead of comparing full-disk images. In order to decrease the retrieval time, we used a fully-connected autoencoder, which reduced the 256-element descriptor to a 32-element semantic hash. The performed experiments and comparisons proved the efficiency of the proposed approach. Our approach has the highest precision value in comparison with other state-of-the-art methods. The presented method can be used not only for solar image retrieval but also for classification tasks.

Moving Object Detection for Complex Scenes by Merging BG Modeling and Deep Learning Method

“…Object detection and classification are two essential tasks in many applications [1,2,3,4]. Especially in intelligent transportation system and surveillance challenges, moving object detection can be regarded as fundamental and important research field.…”