No abstract

No abstract

This paper presents a comparative study of using different color systems on watermarking algorithms. This comparison aim is to determining the robustness and the stability of the color systems used in the watermarking scheme. The watermarking algorithm that is used in this paper is a hybrid scheme using the Discrete Wavelet Transform (DWT) in the Discrete Cosine Transform (DCT) domain. The DCT-DWT watermarking algorithm is applied using three color systems, the RGB (Red, Green and Blue) color system, the HSV (Hue, Saturation and Value) color system and the YIQ color system. The comparison is based on visualization to detect any degradation in the watermarked image, the Peak Signal-to-Noise Ratio (PSNR) of the watermarked image, the Normalized Correlation (NC) of the extracted watermark after extraction, the embedding algorithm CPU time, and applying different types of attacks and then calculating the PSNR and the NC.

Feature extraction is an important process in image classification for achieving an efficient accuracy for the classification learning models. One of these methods is using the convolution neural networks. The use of the trained classic deep convolution neural networks as features extraction gives a considerable results in the remote sensing images classification models. So, this paper proposes three classification approaches using the support vector machine where based on the use of the ImageNet pre-trained weights classic deep convolution neural networks as features extraction from the remote sensing images. There are three convolution models that used in this paper; the Densenet 169, the VGG 16, and the ResNet 50 models. A comparative study is done by extract features using the outputs of the mentioned ImageNet pre-trained weights convolution models after transfer learning, and then use these extracted features as input features for the support vector machine classifier. The used datasets in this paper are the UC Merced land use dataset and the SIRI-WHU dataset. The comparison is based on calculating the overall accuracy to assess the classification model performance.

No abstract