Atanassov's intuitionistic fuzzy histon for robust moving object detection

Giveki, Davar; Montazer, Gholam Ali; Soltanshahi, Mohammad Ali

doi:10.1016/j.ijar.2017.08.014

Cited by 14 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Comparison results of our proposed method with other method in the case of image retrieval for Corel‐1k dataset 5,17,19–21,24,39–41 …”

Section: Experimental Results and Comparisonsmentioning

confidence: 99%

“…Various applications of computer vision such as moving object detection, image classification and content based image retrieval (CBIR) are considered as vital and challenging problems that have absorbed much more attention in the recent years 1–12 . Therefore, considering the importance of image classification and CBIR, designing new methods with higher performance is highly demanded.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A new method for image classification and image retrieval using convolutional neural networks

Giveki

Shakarami²,

Tarrah

et al. 2021

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

This article proposes a new method for image classification and image retrieval. The advantages of the proposed method are its high performance and requiring less memory compared to other methods. In order to extract image features, a Convolutional Neural Network (CNN), AlexNet, has been used. For image classification, we design a committee of four classifiers trained on graphics cards, narrowing the gap to human performance. For image retrieval, the similarity between extracted features from dataset images and features of the query image is calculated and the final results are visualized. Comprehensive experiments on Corel‐1k, Corel‐10k, Caltech‐101 object and Scene‐67 datasets have been investigated to find optimal parameters of the proposed method. The experiments demonstrate the high performance of the proposed method in comparison with the state‐of‐the‐art in the field.

show abstract

“…Comparison results of our proposed method with other method in the case of image retrieval for Corel‐1k dataset 5,17,19–21,24,39–41 …”

Section: Experimental Results and Comparisonsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new method for image classification and image retrieval using convolutional neural networks

Giveki

Shakarami²,

Tarrah

et al. 2021

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

show abstract

“…Videos used for comparison, such as 'highway', 'pedestrian', 'backdoor' and 'bungalows' are collected from CD.net dataset and simulated rain streaks with different patterns are added. The performance evaluation for background as well as foreground detection can be measured in terms of parameters such as f 0 measure, f 1 measure and f j measure [52], [30]. These parameters can be formulated as follows.…”

Section: ) Synthetic Datamentioning

confidence: 99%

An Automated Unified Framework for Video Deraining and Simultaneous Moving Object Detection in Surveillance Environments

Baiju

George

2020

IEEE Access

View full text Add to dashboard Cite

In many instances, outdoor surveillance systems suffer from atrocious weather conditions such as rain, since images or videos captured by such vision systems in rainy days may undergo severe visual dilapidations. This can cause a glitch in those algorithms which are further used for object detection and tracking. Therefore, an ancillary video processing algorithm namely, video deraining is necessary prior to the implementation of object detection and tracking. This indicates the requirement of a time-consuming and complicated two-step process for the detection of moving objects in a rainy environment. This paper proposes an automated single-stage formulation for the conventional three-stage procedure such as rain steak removal, original data recovery and moving object detection as simultaneous operation in the tensor framework. The brilliance of this work is confined in the efficient formulation of an operator termed as Slice Rotational Total Variation (SRTV), to unify the different rain patterns into a common pattern so that any form of rain pattern can be effectively removed from the rainy data in a visually appealing manner by preserving the important details of the data. In this paper, SRTV regularization and tensor low-rank minimization are utilized for the effective deraining as well as efficient retrieval of clean background. Besides, l 1 norm and Tensor Total Variation (TTV) regularizers together with SRTV regularizer are employed for the faithful detection of derained moving objects. The experimental results show that the proposed method outperforms the state of the art methods in terms of deraining capability and accurate moving object detection.

show abstract

Scene classification using a new radial basis function classifier and integrated SIFT–LBP features

Giveki

Karami

2020

Pattern Anal Applic

View full text Add to dashboard Cite

Atanassov's intuitionistic fuzzy histon for robust moving object detection

Cited by 14 publications

References 24 publications

A new method for image classification and image retrieval using convolutional neural networks

A new method for image classification and image retrieval using convolutional neural networks

An Automated Unified Framework for Video Deraining and Simultaneous Moving Object Detection in Surveillance Environments

Scene classification using a new radial basis function classifier and integrated SIFT–LBP features

Contact Info

Product

Resources

About