Combined Rotation- and Scale-Invariant Texture Analysis Using Radon-Based Polar Complex Exponential Transform

Singh, Satya P.; Urooj, Shabana

doi:10.1007/s13369-015-1645-6

Cited by 16 publications

(2 citation statements)

References 37 publications

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“…Based on the intensity levels given by the texture operation, it is useful to extract shape features. The best Fourier moment and shape descriptor suitable for rotation invariant pattern recognition, image analysis and extract shape features is the Accurate Exponential Fourier moment [24]. Hence Fast and Accurate Exponent Fourier moments presents itself to be a relatively constructive shape descriptor for bringing out information of shape texture descriptor.…”

Section: Integration Of the Wavelet Transform Lbp And Fast And Accurmentioning

confidence: 99%

Content-Based Image Retrieval System Based on Fusion of Wavelet Transform, Texture and Shape Features

Kishore¹,

Rao²

2021

MMEP

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In the last few years, Content-Based Image Retrieval (CBIR) has received wide attention. Compared to text-based image retrieval contents of the image are more in information for efficient retrieval by Content-Based Image Retrieval. The single feature cannot be applied to all the images and provides lower performance. In this paper, we have put forward a proposal on an image retrieval using multi-feature fusion. The concept of multi-resolution has been exploited with the help of a wavelet transform. This method combines Local Binary Pattern (LBP) with Fast and Accurate Exponent Fourier Moments (FAEFM’s) with the wavelet decomposition of an image using multiple resolutions. In order to extract the feature of texture from image, LBP codes of Discrete Wavelet Transform (DWT), the image coefficients are estimated followed by the computation of Fast and Accurate Exponent Fourier Moments to these LBP codes so as to extract features of shape to construct the required feature vector. These constructed vectors aid us in exactly finding out and retrieving visually similar images from existing databases. The benchmark databases Corel-1k and Olivia 2688 are used to test the proposed method. The proposed method achieves 99.99% of precision and 93.15% of recall on Corel-1k database and 99.99% of precision and recall of 93.63% on Olivia-2688 database, which are higher than the existing methods.

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Section: Integration Of the Wavelet Transform Lbp And Fast And Accurmentioning

confidence: 99%

Content-Based Image Retrieval System Based on Fusion of Wavelet Transform, Texture and Shape Features

Kishore¹,

Rao²

2021

MMEP

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“…Moments and invariants of images are very useful for invariant feature extraction due to their rotation, scale, and translation invariance properties [21]. Geometric invariance is one of the basic common senses and is used in many object recognition and image matching [22], [23].…”

Section: Introductionmentioning

confidence: 99%

Template Matching Based on Geometric Invariance in Deep Neural Network

et al. 2019

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Machine learning models, especially deep neural networks (DNNs), have achieved state of the art in computer vision and speech recognition. However, with wide applications of DNNs, some problems have appeared, such as lack of interpretability and vulnerable to adversarial examples. Whether the judgment of the model is consistent with that of human is a key to the wide application and development of neural networks. In this paper, we propose a novel and interpretable method to enable the model to make the same judgment as humans in the adversarial examples, which is based on the geometric invariance between images of the same category. Template matching is combined with convolution neural network during the training and testing stage. Moreover, we manage to give a theorical proof. The geometric invariance features got from the template matching are fused with the features extracted by the convolutional layers. The experimental results demonstrate the temp_model (network added the template matching) has a higher test accuracy both on benchmark sequences and adversarial examples, and we use a visual method to explain the reason why adding template can make the network perform better. The generality and convergence of the network improve without increasing the model size and training time after adding the template as common sense.INDEX TERMS Deep neural network, geometric invariant, interpretability, template match, adversarial example.

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