Intelligent object recognition in underwater images using evolutionary-based Gaussian mixture model and shape matching

Kannan, Srividhya

doi:10.1007/s11760-019-01619-w

Cited by 18 publications

(7 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GMM is applied to study the distribution parameters based on the optimal threshold that corresponds to the minimum calculated error probability [21]. GMM is an accurate method and the number of clusters is predetermined [30]. This is in line with the opinion [31] that GMM is a method that can be used for data clustering.…”

Section: Gmmmentioning

confidence: 83%

Hardware sales forecasting using clustering and machine learning approach

Puspita

Wulandhari

2022

IJ-AI

View full text Add to dashboard Cite

This research is a case study of an information technology (IT) solution company. There is a problem that is quite crucial in the hardware sales strategy which makes it difficult for the company to predict the number of various items that will be sold and also causes the excess or shortage in hardware stocking. This research focuses on clustering to group various of items and forecast the number of items in each cluster using a machine learning approach. The methods used in clustering are k-means clustering, agglomerative hierarchical clustering (AHC), and gaussian mixture models (GMM), and the methods used in forecasting are autoregressive integrated moving average (ARIMA) and recurrent neural network-long short-term memory (RNN-LSTM). For clustering, k-means uses two attributes, namely "Quantity and Stock" as the best feature in this case study. Using these features the k-means obtain silhouette results of 0.91 and davies bouldin index (DBI) values of 0.34 consisting of 3 clusters. While for forecasting, RNN-LSTM is the best method, where it produces more cost savings than the ARIMA method. The percentage of the difference in saving costs between ARIMA and RNN-LSTM to the actual cost is 83%.

show abstract

Section: Gmmmentioning

confidence: 83%

Hardware sales forecasting using clustering and machine learning approach

Puspita

Wulandhari

2022

IJ-AI

View full text Add to dashboard Cite

show abstract

“…Gaussian mixture model provides better performance during detection and recognition process. The control parameters are optimized through GA [ 93 ].…”

Section: Applicationsmentioning

confidence: 99%

A review on genetic algorithm: past, present, and future

Katoch

Chauhan

Kumar

2020

Multimed Tools Appl

2,473

761

View full text Add to dashboard Cite

In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis. This review will help the new and demanding researchers to provide the wider vision of genetic algorithms. The well-known algorithms and their implementation are presented with their pros and cons. The genetic operators and their usages are discussed with the aim of facilitating new researchers. The different research domains involved in genetic algorithms are covered. The future research directions in the area of genetic operators, fitness function and hybrid algorithms are discussed. This structured review will be helpful for research and graduate teaching.

show abstract

“…To address this problem, some authors tend to use statistical models. For example, Kannan [13] has attempted to recognize objects in seawater images using a Gaussian mixture model in combination with optimization methods like the genetic algorithm. Although these traditional inner distance shape matching techniques have made some progress in certain specific situations, they usually have lower underwater image recognition accuracy, when compared to the most recent deep learning algorithms.…”

Section: Fish Segmentationmentioning

confidence: 99%

Distributed Deep Learning and Energy-Efficient Real-Time Image Processing at the Edge for Fish Segmentation in Underwater Videos

et al. 2022

View full text Add to dashboard Cite

Using big marine data to train deep learning models is not efficient, or sometimes even possible, on local computers. In this paper, we show how distributed learning in the cloud can help more efficiently process big data and train more accurate deep learning models. In addition, marine big data is usually communicated over wired networks, which if possible to deploy in the first place, are costly to maintain. Therefore, wireless communications dominantly conducted by acoustic waves in underwater sensor networks, may be considered. However, wireless communication is not feasible for big marine data due to the narrow frequency bandwidth of acoustic waves and the ambient noise. To address this problem, we propose an optimized deep learning design for low-energy and real-time image processing at the underwater edge. This leads to trading the need to transmit the large image data, for transmitting only the low-volume results that can be sent over wireless sensor networks. To demonstrate the benefits of our approaches in a real-world application, we perform fish segmentation in underwater videos and draw comparisons against conventional techniques. We show that, when underwater captured images are processed at the collection edge, 4 times speedup can be achieved compared to using a landside server. Furthermore, we demonstrate that deploying a compressed DNN at the edge can save 60% of power compared to a full DNN model. These results promise improved applications of affordable deep learning in underwater exploration, monitoring, navigation, tracking, disaster prevention, and scientific data collection projects. INDEX TERMSBig marine data, Deep learning, Edge computing, Fish image segmentation, Internet of underwater things, Real-time video processing, U-Net convolutional neural networks.

show abstract

Intelligent object recognition in underwater images using evolutionary-based Gaussian mixture model and shape matching

Cited by 18 publications

References 28 publications

Hardware sales forecasting using clustering and machine learning approach

Hardware sales forecasting using clustering and machine learning approach

A review on genetic algorithm: past, present, and future

Distributed Deep Learning and Energy-Efficient Real-Time Image Processing at the Edge for Fish Segmentation in Underwater Videos

Contact Info

Product

Resources

About