An Entropy-Histogram Approach for Image Similarity and Face Recognition

Al-Janabi, M.; Hussain, Zahir M.; Lu, Songfeng

doi:10.1155/2018/9801308

Cited by 22 publications

(20 citation statements)

References 25 publications

(28 reference statements)

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“…Signal similarity is the science of finding similarity between signals that are probably delayed or affected by noise, blur, or other kinds of distortion (Wang et al, 2004;Chalom et al, 2013;Shnain et al, 2017;Aljanabi et al, 2018;Hassan et al, 2014;Hashim and Hussain, 2014). This topic has many applications in security, object recognition, medical diagnosis and many other applications.…”

Section: Introductionmentioning

confidence: 99%

“…One of the main applications of image similarity is in face recognition (Shnain et al, 2017;Aljanabi et al, 2018;Hassan et al, 2014;Hashim and Hussain, 2014). One of the main streams in face recognition is to recognize a given face image in the sense of similarity with some image in a large face-database.…”

Section: Introductionmentioning

confidence: 99%

“…Similarity measure is a mathematical technique that can detect similarity between signals in a way similar to human recognition (Wang et al, 2004). Similarity measures can be based on correlative, transformational or statistical analysis (Shnain et al, 2017;Aljanabi et al, 2018). The conventional metrics such as the Peak Signal-To-Noise Ratio (PSNR) and the Mean Squared Error (MSE) operate directly on the intensity of the image and they do not correlate well with the subjective fidelity ratings.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, many efforts have been made to design Image Quality Assessment (IQA) metrics that are based on the functions performed by The Human Visual System (HVS). Such kinds of models emphasize the importance of HVS sensitivity to different visual signals, such as luminance, contrast and frequency content (Wang et al, 2004;Chalom et al, 2013;Shnain et al, 2017;Aljanabi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…In 2017, A novel feature-based measure (FSM) that combines the best features of the well-known SSIM and FSIM approaches was proposed, striking a balance between their performances for similar and dissimilar images (Shnain et al, 2017). In 2018, an informationtheoretic measure based on the joint histogram was proposed in (Aljanabi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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SVD-Structural Similarity in the Wavelet-Gabor Domain: Improved Confidence for Face Recognition under Noise, Blur and Haze

Ismael¹,

Hussain²

2019

Journal of Computer Science

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In this work we propose and investigate the performance of a new similarity measure based on Singular Value Decomposition (SVD) and structural similarity in the wavelet and Gabor domains. The reasoning behind this combination is to utilize SVD in getting independent components, wavelet decomposition to get the complex frequency features and Gabor filtering to get textural features. A comparison has been made versus correlative and structural similarity measures like SSIM (Structural Similarity Index Measure), Complex-Wavelet SSIM (CWSSIM) and FSIM (Feature-Based Similarity). In these tests, a reference image is tested for similarity against several face images in a database under adverse conditions like noise, blur and haze. A new haze formation approach has also been proposed. Similarity level and similarity confidence are taken as the performance measures. Two confidence measures, different in strength of confidence, have been proposed and tested versus a recently-proposed confidence measure that relies on the difference between the maximal similarity (best match in the database) minus the second maximum similarity (second-best match in the database). Simulation using AT&T database has shown that the proposed SVD-Structural Similarity in Wavelet-Gabor Domain (SVWG) outperforms existing measures by far. SVWG can give more robust decisions (near-optimal confidence); also, can work under more adverse conditions (lower SNR, more blur or haze) where other similarity measures fail.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SVD-Structural Similarity in the Wavelet-Gabor Domain: Improved Confidence for Face Recognition under Noise, Blur and Haze

Ismael¹,

Hussain²

2019

Journal of Computer Science

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Learning Hierarchical Semantic Correspondence and Gland Instance Segmentation

Wang

Chung

2020

Machine Learning in Medical Imaging

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The morphology statistics of colon glands is a key feature for pathologists to diagnose colorectal cancer. Current gland instance segmentation methods show good overall performances, but accurate segmentation of extremely deformed glands in highly malignant cases or some rare benign cases remains to be challenging . In this paper, we propose a hybrid model that learns hierarchical semantic feature matching from histological pairs in an attentive process, where both spatial details and morphological appearances can be well preserved and balanced, especially for the glands with severe deformation. A consistency loss function is also introduced to enforce simultaneous satisfaction of semantic correspondence and gland instance segmentation on the pixellevel. The novel proposed model is validated on two publicly available colon gland datasets GlaS and CRAG. The model successfully boosts the segmentation performances on greatly mutated or deformed cases, and outperforms the state-of-the-art approaches.

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Image Segmentation Approach Based on Hybridization Between K-Means and Mask R-CNN

Moussaoui

Benslimane

Akkad

2021

Lecture Notes in Electrical Engineering

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In this article, we will introduce a hybrid method based on the combination of two image segmentation techniques. The first method adopted is the k-means algorithm which is an unsupervised machine learning technique used to group data points, the second is the Mask R-CNN which is a neural network architecture which combines two sub-problems: object detection and semantic segmentation. The main objective of this study is to approve the segmentation of the image using k-means. The first step is to apply Mask R-CNN on our original image to detect the objects that are present in the image, then, we will apply k-means clustering to have better segmentation. For our approach we used a set of metrics to evaluate our proposed approach such as the mean square error (MSE); the peak signal to noise ratio (PSNR) and many other measures such as the difference between two images. These measurements have shown satisfactory results and a performance of the proposed method.

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An Entropy-Histogram Approach for Image Similarity and Face Recognition

Cited by 22 publications

References 25 publications

SVD-Structural Similarity in the Wavelet-Gabor Domain: Improved Confidence for Face Recognition under Noise, Blur and Haze

SVD-Structural Similarity in the Wavelet-Gabor Domain: Improved Confidence for Face Recognition under Noise, Blur and Haze

Learning Hierarchical Semantic Correspondence and Gland Instance Segmentation

Image Segmentation Approach Based on Hybridization Between K-Means and Mask R-CNN

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