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1

Semantic hashing for fast solar magnetogram retrieval

Grycuk Rafał, Scherer Rafał, Marchlewska Alina, Napoli Christian

Journal of Artificial Intelligence and Soft Computing Research

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2022

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Vol. 12, No. 4

299--306

EN

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.

2

Radon‐wavelet based novel image descriptor for mammogram mass classification

Obaidullah Sk Md, Ahmed Sajib, Gonçalves Teresa, Rato Luís

Journal of Automation Mobile Robotics and Intelligent Systems

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2020

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Vol. 14, No. 2

74--80

EN

Mammography based breast cancer screening is very popular because of its lower costing and readily availability. For automated classification of mammogram images as benign or malignant machine learning techniques are involved. In this paper, a novel image descriptor which is based on the idea of Radon and Wavelet transform is proposed. This method is quite efficient as it performs well without any clinical information. Performance of the method is evaluated using six different classifiers namely: Bayesian network (BN), Linear discriminant analysis (LDA), Logistic, Support vector machine (SVM), Multilayer perceptron (MLP) and Random Forest (RF) to choose the best performer. Considering the present experimental framework, we found, in terms of area under the ROC curve (AUC), the proposed image descriptor outperforms, upto some extent, previous reported experiments using histogram based hand‐crafted methods, namely Histogram of Oriented Gradient (HOG) and Histogram of Gradient Divergence (HGD) and also Convolution Neural Network (CNN). Our experimental results show the highest AUC value of 0.986, when using only the carniocaudal (CC) view compared to when using only the mediolateral oblique (MLO) (0.738) or combining both views (0.838). These results thus proves the effectiveness of CC view over MLO for better mammogram mass classification.

3

Fast image index for database management engines

Grycuk Rafał, Najgebauer Patryk, Kordos Miroslaw, Scherer Magdalena M., Marchlewska Alina

Journal of Artificial Intelligence and Soft Computing Research

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2020

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Vol. 10, No. 2

113--123

EN

Large-scale image repositories are challenging to perform queries based on the content of the images. The paper proposes a novel, nested-dictionary data structure for indexing image local features. The method transforms image local feature vectors into two-level hashes and builds an index of the content of the images in the database. The algorithm can be used in database management systems. We implemented it with an example image descriptor and deployed in a relational database. We performed the experiments on two image large benchmark datasets.

4

Detecting visual objects by edge crawling

Grycuk Rafał, Wojciechowski Adam, Wei Wei, Siwocha Agnieszka

Journal of Artificial Intelligence and Soft Computing Research

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2020

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Vol. 10, No. 3

223--237

EN

Content-based image retrieval methods develop rapidly with a growing scale of image repositories. They are usually based on comparing and indexing some image features. We developed a new algorithm for finding objects in images by traversing their edges. Moreover, we describe the objects by histograms of local features and angles. We use such a description to retrieve similar images fast. We performed extensive experiments on three established image datasets proving the effectiveness of the proposed method.