Modeling global geometric spatial information for rotation invariant classification of satellite images

An efficient framework is developed by deep neural networks (DNNs) and artificial neural network (ANNs) for predicting the outcomes of football matches. A dataset is used with the rankings, team performances, all previous international football match results and so on. ANN and DNN are used to explore and process the sporting data to generate prediction value. Datasets are divided into sections for training, validating and testing. By using the proposed DNN architecture, corresponding model performed excellently on predicting the FIFA world cup 2018 matches. This model had predicted 63.3% matches accurately. However, this accuracy can be increased with proper datasets and more accurate information of the teams. The outcome of this hypothesis can be derived that deep learning may be used for successfully predicting the outcomes of football matches or any other sporting events. For more accurate performance of the prediction, prior and more information about each team, player and match is desirable.

Section: Related Workmentioning

confidence: 99%

A deep learning framework for football match prediction

Rahman

2020

“…Histograms of triangular regions and relative spatial information for histogrambased representation of the BoVW (Bag of visual words) model are reported in Ali et al [23,24] and Zafar et al [47][48][49] respectively. Feature computation based on spatial information is reported in Zafar et al [47][48][49], Latif et al [50] and Ali et al [51].…”

Section: Related Workmentioning

confidence: 99%

An efficient content based image retrieval using enhanced multi-trend structure descriptor

Sathiamoorthy

Mohananthini

2020

This paper contributes a new variant of multi-trend structure descriptor (MTSD) for efficient content based image retrieval. The proposed variant of MTSD encodes color/edge orientation/texture quantized values versus orientation of equal, small and large trends instead of color/edge orientation/texture quantized values versus equal, small and large trends. In addition, it also encodes color/edge orientation/texture quantized values versus average location of distribution of pixel values for equal, small and large trends at each orientation. To reduce the time cost of the proposed variant of MTSD with the preservation of its accuracy, the image is decomposed into fine level using discrete Haar wavelet transform and the fine level for the decomposition of an image is determined empirically. Comprehensive experiments are conducted using the benchmark Corel-1k, Corel-5k, Corel-10k, Caltech-101, LIDC-IDRI-CT, VIA/I-ELCAP-CT and OASIS-MRI image datasets and the results evident that the proposed variant of MTSD achieves the state-of-the-art performance for natural, textural and biomedical image retrieval. Precision and recall are the measures used to measure the accuracy. Euclidean similarity measure is used to calculate the similarity information between query and target images.

“…Besides, a method using SVM was proposed for satellite image classification based on Pairs Orthogonal Vector Histogram (POVH). The advantage is computing the discriminative spatial clues, which is robust to image rotation and getting better performance than CNN [33]. Many parallel versions of SVM have been proposed.…”

Section: Related Workmentioning

confidence: 99%

A novel parallel learning algorithm for pattern classification

Wang

Wei

2019

In today's data-intensive applications, machine learning constructs algorithms that are capable of learning and making predications on the data. Margin setting algorithm (MSA) is a novel machine learning algorithm for pattern classification. It employs an artificial immune system approach to generates prototype regions as the classification boundaries. However, its computation time limited its applications in real-world application. When the datasets grow in size and algorithm complexity increases, it is necessary to spread the work among multiple cores and processors. To reduce the execution time during classification, a parallel implementation of MSA, called PMSA is proposed for multicore and multiprocessor system. It is the first work to scale up the classification time of MSA using parallel implementation. To evaluate the proposed PMSA algorithm, we used standard image datasets of 512 × 512 pixels and 321 × 481 pixels. Besides, benchmark datasets from University of California, Irvine Machine Learning Repository are also used. They are 768 data samples from dataset Pima Indian Diabetes, 683 data samples from dataset Wisconsin Breast Cancer, 690 data samples from dataset Australian Credit Approval, 178 data samples from dataset Wine and 391 data samples from dataset Svmguide2. The classification performance is compared with another two state-of-the-art classification algorithms: the artificial neural network and the support vector machine. The results show the proposed PMSA gains significant improvements in terms of execution time, with a promising speedup compared to the single-threaded CPU counterpart.