Hamid A. Jalab

Image retrieval system based on color layout descriptor and Gabor filters

The current paper presents a content-based image retrieval (CBIR) system using the image features extracted by a color layout descriptor (CLD) and Gabor texture descriptor. CLD represents the spatial distribution of colors with a few nonlinear quantized DCT coefficients of grid-based average colors, whereas the Gabor filter works as a bandpass filter for the local spatial frequency distribution. These two descriptors are very powerful for CBIR systems. Furthermore, combining the color and texture features in CBIR systems leads to more accurate results for image retrieval. To compare the performance of image retrieval method, average precision and recall are computed for all queries. The results showed an improved performance (higher precision and recall values) compared with the performance using other CBIR methods.

Denoising Algorithm Based on Generalized Fractional Integral Operator with Two Parameters

In this paper, a novel digital image denoising algorithm called generalized fractional integral filter is introduced based on the generalized Srivastava-Owa fractional integral operator. The structures of fractional masks of this algorithm are constructed. The denoising performance is measured by employing experiments according to visual perception and PSNR values. The results demonstrate that apart from enhancing the quality of filtered image, the proposed algorithm also reserves the textures and edges present in the image. Experiments also prove that the improvements achieved are competent with the Gaussian smoothing filter.

Skin segmentation based on multi pixel color clustering models

This paper presents a reliable color pixel clustering model for skin segmentation under unconstrained scene conditions. The proposed model can overcome sensitivity to variations in lighting conditions and complex backgrounds. Our approach is based on building multi-skin color clustering models using the Hue, Saturation, and Value color space and multi-level segmentation. Skin regions are extracted using four skin color clustering models, namely, the standard-skin, shadow-skin, light-skin, and high-red-skin models. Moreover, skin color correction (skin lighting) at the shadow-skin layer is used to improve the detection rate. The experimental results from a large image data set demonstrate that the proposed clustering models could achieve a true positive rate of 96.5% and a false positive rate of approximately 0.765%. The experimental results show that the color pixel clustering model is more efficient than other approaches.

Texture Enhancement Based on the Savitzky-Golay Fractional Differential Operator

Texture enhancement for digital images is the most important technique in image processing. The purpose of this paper is to design a texture enhancement technique using fractional order Savitzky-Golay differentiator, which leads to generalizing the Savitzky-Golay filter in the sense of the Srivastava-Owa fractional operators. By employing this generalized fractional filter, texture enhancement is introduced. Consequently, it calculates the generalized fractional order derivative of the given image using the sliding weight window over the image. Experimental results show that the operator can extract more subtle information and make the edges more prominent. In general, the capability of the generalized fractional differential will be high because it is sensitive to the subtle fluctuations of values of pixels.

Fractional Alexander polynomials for image denoising

Image denoising is an important task in image processing. The interest in using a fractional mask window operator based on fractional calculus has grown for image denoising. This paper mainly introduces the concept of fractional calculus and proposes a new mathematical method in using fractional Alexander polynomials for image denoising. The structures of ni?n fractional mask windows on eight directions of this algorithm are constructed. Finally, we measure the denoising performance by employing experiments based on visual perception and by using peak signal-to-noise ratios. The experiments illustrate that the improvements achieved are compatible with other standard smoothing filters. An image denoising algorithm based on fractional Alexander polynomials is proposed.The denoising algorithm relies on the optimal values of fractional power parameters α and t.The denoising performance is measured based on the visual perception and the PSNR.Experiments demonstrate that the improvements achieved are compatible with the standard filters.

Fractional Differential Texture Descriptors Based on the Machado Entropy for Image Splicing Detection

Image splicing is a common operation in image forgery. Different techniques of image splicing detection have been utilized to regain people’s trust. This study introduces a texture enhancement technique involving the use of fractional differential masks based on the Machado entropy. The masks slide over the tampered image, and each pixel of the tampered image is convolved with the fractional mask weight window on eight directions. Consequently, the fractional differential texture descriptors are extracted using the gray-level co-occurrence matrix for image splicing detection. The support vector machine is used as a classifier that distinguishes between authentic and spliced images. Results prove that the achieved improvements of the proposed algorithm are compatible with other splicing detection methods.

Existence of Ulam Stability for Iterative Fractional Differential Equations Based on Fractional Entropy

In this study, we introduce conditions for the existence of solutions for an iterative functional differential equation of fractional order. We prove that the solutions of the above class of fractional differential equations are bounded by Tsallis entropy. The method depends on the concept of Hyers-Ulam stability. The arbitrary order is suggested in the sense of Riemann-Liouville calculus.

Improving RLRN Image Splicing Detection with the Use of PCA and Kernel PCA

Digital image forgery is becoming easier to perform because of the rapid development of various manipulation tools. Image splicing is one of the most prevalent techniques. Digital images had lost their trustability, and researches have exerted considerable effort to regain such trustability by focusing mostly on algorithms. However, most of the proposed algorithms are incapable of handling high dimensionality and redundancy in the extracted features. Moreover, existing algorithms are limited by high computational time. This study focuses on improving one of the image splicing detection algorithms, that is, the run length run number algorithm (RLRN), by applying two dimension reduction methods, namely, principal component analysis (PCA) and kernel PCA. Support vector machine is used to distinguish between authentic and spliced images. Results show that kernel PCA is a nonlinear dimension reduction method that has the best effect on R, G, B, and Y channels and gray-scale images.

Texture Enhancement for Medical Images Based on Fractional Differential Masks

Texture enhancement for medical images is the most important technique in medical image diagnosis. This paper introduces a texture enhancement technique for medical images by using fractional differential (FD) masks based on Srivastava-Owa fractional operators. We also construct a 2D isotropic gradient mask based on generalized fractional operators. Texture enhancement performance is measured by applying experiments according to visual perception and by using Sobel/Canny edge filters and gray-level co-occurrence matrix. We discuss the capability of the FD mask for texture enhancement. The experiments and analysis show that the operator can extract subtle information and make the edges prominent.

Time-Space Fractional Heat Equation in the Unit Disk

We will study a maximal solution of the time-space fractional heat equation in complex domain. The fractional time is taken in the sense of the Riemann-Liouville operator, while the fractional space is assumed in the Srivastava-Owa operator. Here we employ some properties of the univalent functions in the unit disk to determine the upper bound of this solution. The maximal solution is illustrated in terms of the generalized hypergeometric functions.