scholarly journals Common Gabor Features for Image Watermarking Identification

2021 ◽  
Vol 11 (18) ◽  
pp. 8308
Author(s):  
Ismail Taha Ahmed ◽  
Baraa Tareq Hammad ◽  
Norziana Jamil
Keyword(s):  
Feature Vector ◽  
Image Watermarking ◽  
Mean Amplitude ◽  
Performance Outcomes ◽  
Public Database ◽  
Major Goal ◽  
Feature Sets ◽  
Gabor Features ◽  
Forest Use ◽  
Combined Feature

Image watermarking is one of many methods for preventing unauthorized alterations to digital images. The major goal of the research is to find and identify photos that include a watermark, regardless of the method used to add the watermark or the shape of the watermark. As a result, this study advocated using the best Gabor features and classifiers to improve the accuracy of image watermarking identification. As classifiers, discriminant analysis (DA) and random forests are used. The DA and random forest use mean squared energy feature, mean amplitude feature, and combined feature vector as inputs for classification. The performance of the classifiers is evaluated using a variety of feature sets, and the best results are achieved. In order to assess the performance of the proposed method, we use a public database. VOC2008 is a public database that we use. The findings reveal that our proposed method’s DA classifier with integrated features had the greatest TPR of 93.71 and the lowest FNR of 6.29. This shows that the performance outcomes of the proposed approach are consistent. The proposed method has the advantages of being able to find images with the watermark in any database and not requiring a specific type or algorithm for embedding the watermark.

scholarly journals Prediction of Cup-to-Disc Ratio From Optic Fundus Image

2021 ◽  
Author(s):  
Jermaine Ramdass
Keyword(s):  
Feature Vector ◽  
Information Gain ◽  
Image Features ◽  
Main Step ◽  
Support Vector ◽  
Fundus Image ◽  
Chi Square ◽  
Vector Machines ◽  
Combined Feature ◽  
Cup To Disc Ratio

A technique is proposed that can be used to predict the cup-to-disc ratio from a single optic fundus image and determine which image features have the highest contribution to a specific ophthalmologist’s measured cup-to-disc ratio. The procedure starts with image pre-processing. The main step of the procedure is feature extraction where image features related to pixel intensities are found. These features are used to train three different classifiers: neural networks, support vector machines, and sparse representation classifiers. The classifiers are tested and evaluated to see how accurately they can predict the cup-to-disc ratio. The best obtained results are in the 70-75% success range. Finally, feature ranking is performed using the methods of chi square and information gain on a combined feature vector using measured cup-to-disc ratios from each ophthalmologist to determine the importance and contribution of each feature to that ophthalmologist.

scholarly journals Intelligent Music Player Based on Emotions

2020 ◽  
Vol 9 (3) ◽  
pp. 525-528
Keyword(s):  
Facial Expressions ◽  
Feature Vector ◽  
Emotional Experience ◽  
Head Movements ◽  
Cascade Classifier ◽  
Feature Level Fusion ◽  
Web Camera ◽  
Music Player ◽  
Combined Feature ◽  
Level Fusion

This project presents a system to automatically detect emotional dichotomy and mixed emotional experience using a Linux based system. Facial expressions, head movements and facial gestures were captured from pictorial input in order to create attributes such as distance, coordinates and movement of tracked points. Web camera is used to extract spectral attributes. Features are calculated using Fisherface algorithm. Emotion detected by cascade classifier and feature level fusion was used to create a combined feature vector. Live actions of user are to be used for recording emotions. As per calculated result system will play songs and display books list.

INFRARED FACE RECOGNITION BASED ON LOG-GABOR WAVELETS

2006 ◽  
Vol 20 (03) ◽  
pp. 351-360 ◽  
Author(s):  
XUERONG CHEN ◽  
ZHONGLIANG JING
Keyword(s):  
Face Recognition ◽  
Feature Vector ◽  
Face Image ◽  
Gabor Wavelets ◽  
Promising Alternative ◽  
Gabor Features ◽  
Gabor Feature ◽  
Human Faces ◽  
Visible Imagery ◽  
Log Gabor

Despite the variety of approaches and tools studied, face recognition is not accurate or robust enough to be used in uncontrolled environments. Recently, infrared (IR) imagery of human faces is considered as a promising alternative to visible imagery. IR face recognition is a biometric which offers the security of fingerprints with the convenience of face recognition. However, IR has its own limitations. The presence of eyeglasses has more influence on IR than visible imagery. In this paper, a method based on Log-Gabor wavelets for IR face recognition is proposed. The method first derives a Log-Gabor feature vector from IR face image, then obtains the independent Log-Gabor features by using independent component analysis (ICA). Experimental results show that the proposed method works well, even in challenging situations.

scholarly journals Content-Based Image Retrieval with Combined Features: Color and Gradient

2021 ◽  
Author(s):  
Mahmut Kılıçaslan ◽  
Recep Demirci
Keyword(s):  
Image Retrieval ◽  
Feature Vector ◽  
Color Image ◽  
Content Based Image Retrieval ◽  
Color Reduction ◽  
Feature Vectors ◽  
Image Histogram ◽  
Histogram Intersection ◽  
Combined Features ◽  
Combined Feature

Abstract Feature extraction is fundamental stage of effective content based image retrieval (CBIR). However, it remains challenging issue to extract low-level features for retrieval systems. This paper puts forward an effective solution proposal for the aforementioned problem. Initially, images and their gradients are clustered with multi-level thresholding. A codebook is generated with threshold values. The size of the codebook generated depends on the number of thresholds. Consequently, every pixel in color image is included in a cluster by means of the codebook. Color reduction is performed by assigning the average values of pixels in the same cluster. A cluster-based one-dimensional histogram (CBH) is created with the numbers of pixels in every cluster represented with a single color. Then the cluster-based feature vectors with histogram are extracted from original image and gradient image. Accordingly, relevant features are combined. The developed feature vector is called as combined feature vector (CFV). The most important advantages of CFV are that it performs an effective color reduction technique and feature presentation by processing texture information with gradient operator. Therefore, the main contribution of the combined feature vector suggested is its high accuracy and stability for image retrieval. The proposed method has been tested with Corel-1K, Corel-5K Corel-10K and GHIM-10K datasets. In addition, performances of different image histogram similarity techniques such as cosine, histogram intersection and Euclidean distance have been verified with the developed algorithm. Experimental results have been analyzed in two categories. Initially, CBIR results produced with combined feature vectors which are generated by Otsu, Kapur and center of gravity of histogram (CGH) procedures have been evaluated. Then, the CBIR strategy based on CGH method has been compared with CBIR systems with local binary pattern (LBP) and gradient-structures histogram (GSH). It was observed that CBIR approach based on CGH technique has significantly outperformed.

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