Image classification based on improved VLAD

Long, Xianzhong; Lu, Hongtao; Peng, Yong; Wang, Xianzhong; Feng, Shaokun

doi:10.1007/s11042-015-2524-6

Cited by 13 publications

(4 citation statements)

References 33 publications

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“…The papers [19][20][21][22][23] present the various approaches which are applied to classify an image as useful or useless. In [24], the author uses low-level features and the OCR technique using SVM classifier to obtain 95 per cent accuracy.…”

Section: Resultsmentioning

confidence: 99%

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Image Spam Filtering using Machine Learning Techniques

2019

ijrte

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Unsolicited visual data is undesirable in any form. The art of hiding malicious content in images and adding them as attachments to electronic mails has become a popular nuisance. In recent years, attackers have developed various new techniques to evade traditional spam classification systems. Text-based spam classification has been in focus for a long time and, researchers have successfully created a prodigal system for identifying spam text in electronic mails using Optical Character Recognition technology. In the last decade, extensive work has been performed to tackle image spam but with unsatisfactory results. Various algorithms and data augmentation techniques are used today to develop an optimal model for image spam recognition. Many of these proposed systems come close to the ideal system but do not provide 100 percent accuracy. This paper highlights the role of three popular techniques in image spam filtering. We discuss the importance and application of Optical Character Recognition, Support Vector Machines and, Artificial Neural Networks in unsolicited visual data filtering. This paper sheds light on the algorithms of these techniques. We provide a comparison of their accuracy, which helps us draw useful insights for developing a robust unsolicited visual data classification system. This paper aims to bring clarity regarding the feasibility of using these techniques to develop an unsolicited visual data filtering system. This paper records that the most favourable results are obtained using Artificial Neural Networks.

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Section: Resultsmentioning

confidence: 99%

“…Overfitting is rectified. It is difficult to accurately define the optimal parameters required for support vector machines [19,20] owing to the high complexity of the algorithm.…”

Section: Fig 4 Algorithm Of Support Vector Machinesmentioning

confidence: 99%

Image Spam Filtering using Machine Learning Techniques

2019

ijrte

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“…This advantage is more obvious when the network input is a multi-dimensional image, so that the image can be directly used as the network input, avoiding the complex feature extraction and data reconstruction process in traditional recognition algorithm. Therefore, convolutional neural networks can also be interpreted as a multilayer perceptron designed to recognize two-dimensional shapes, which are highly invariant to translation, scaling, tilting, or other forms of deformation [7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Research on image classification model based on deep convolution neural network

Xin

Wang

2019

J Image Video Proc.

205

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Based on the analysis of the error backpropagation algorithm, we propose an innovative training criterion of depth neural network for maximum interval minimum classification error. At the same time, the cross entropy and M 3 CE are analyzed and combined to obtain better results. Finally, we tested our proposed M3 CE-CEc on two deep learning standard databases, MNIST and CIFAR-10. The experimental results show that M 3 CE can enhance the cross-entropy, and it is an effective supplement to the cross-entropy criterion. M3 CE-CEc has obtained good results in both databases.

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“…Identifying plants is usually a difficult task, sometimes for professionals (such as farmers or lumberjacks) as well [1]. Using content-based image retrieval technologies is a promising possibility in this field (as a fine-grained object categorization problem [18]), and the aim of our work was to solve it automatically.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Plant Classification and Content-Based Image Retrieval

Gyires-Tóth

Osváth

Papp

et al. 2019

Cybernetics and Information Technologies

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The main goal of the present research is to classify images of plants to species with deep learning. We used convolutional neural network architectures for feature learning and fully connected layers with logsoftmax output for classification. Pretrained models on ImageNet were used, and transfer learning was applied. In the current research image sets published in the scope of the PlantCLEF 2015 challenge were used. The proposed system surpasses the results of all top competitors of the challenge by 8% and 7% at observation and image levels, respectively. Our secondary goal was to satisfy the users’ needs in content-based image retrieval to give relevant hits during species search task. We optimized the length of the returned lists in order to maximize MAP (Mean Average Precision), which is critical to the performance of image retrieval. Thus, we achieved more than 50% improvement of MAP in the test set compared to the baseline.

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Image classification based on improved VLAD

Cited by 13 publications

References 33 publications

Image Spam Filtering using Machine Learning Techniques

Image Spam Filtering using Machine Learning Techniques

Research on image classification model based on deep convolution neural network

Deep Learning for Plant Classification and Content-Based Image Retrieval

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