This paper presents a comparative study using different color spaces to evaluate the performance of color image segmentation using the automatic GrabCut technique. GrabCut is considered as one of the semiautomatic image segmentation techniques, since it requires user interaction for the initialization of the segmentation process. The automation of the GrabCut technique is proposed as a modification of the original semiautomatic one in order to eliminate the user interaction. The automatic GrabCut utilizes the unsupervised Orchard and Bouman clustering technique for the initialization phase. Comparisons with the original GrabCut show the efficiency of the proposed automatic technique in terms of segmentation, quality, and accuracy. As no explicit color space is recommended for every segmentation problem, automatic GrabCut is applied withRGB,HSV,CMY,XYZ, andYUVcolor spaces. The comparative study and experimental results using different color images show thatRGBcolor space is the best color space representation for the set of the images used.

Hyperspectral Unmixing (HU) estimates the combination of endmembers and their corresponding fractional abundances in each of the mixed pixels in the hyperspectral remote sensing image. In this paper, we address the linear unmixing problem with an unsupervised Deep Convolutional Autoencoder network (DCAE). The proposed DCAE is an end-to-end model that consists of two parts: encoder and decoder. First, deep convolutional encoder is employed to extract significant, denoised, non-redundant feature vector. Then one-layer decoder maps the aforementioned feature vector to obtain the endmembers and their abundance percentages. Extensive experiments were carried to evaluate the performance of the proposed method using synthetic and real hyperspectral datasets namely Samson, Cuprite, Urban, and Jasper Ridge. Analyses of the results demonstrate that the proposed DCAE significantly outperforms benchmark unmixing methods even in a noisy environment in terms of both Root Mean Square Error (RMSE), and Mean Square Error (MSE). The achieved results of the proposed DCAE in terms of mean absolute error were 0.0097, 0.001, 0.0141, and 0.0145 for Samson, Cuprite, Urban, and Jasper Ridge datasets respectively.

Recently, transformation‐based methods have been widely used in many computer vision areas because of their powerful representation ability. One of the most widely used transforms is the wavelet transform that has proved to be very useful in many applications. In this study, a new method for human action representation and description is proposed. This method combines the advantages of local and global descriptions. The method works by fusing the Hu invariant moments as global descriptors with a new local descriptor that is based on three‐dimensional stationary wavelet transform and the concept of local binary patterns. The performance of the new method was examined in two different ways. The first one is by fusing the proposed directional global and local features in one feature vector, while the other is using the features of different directional bands separately to train multiple classifiers and then using a voting scheme to vote for the best match. The performance of the proposed method is verified using standard datasets, achieving high accuracy in comparison with state‐of‐the‐art methods. In addition, the proposed method is proved to be robust to the changes in lighting and scale variations, but it exhibits limitations towards dynamic backgrounds.

Nowadays, remote sensing image analysis is needed in various important tasks such as city planning, land-use classification, agriculture monitoring, military surveillance, and many other applications. In this context, hyperspectral images can play a useful role, but require specific handling. This paper presents a convolutional neural network based on one-dimensional support vector machine (SVM) convolution operations (1D-CSVM) for the analysis of hyperspectral images. SVM-based CNN (CSVM) was introduced first for the classification of high spatial resolution RGB images. It relies on linear SVMs to create filter banks in the convolution layers. In this work, the network is modified to cope with one-dimensional hyperspectral signatures and perform pixel-based classification. It thus analyzes each pixel spectrum independently from the pixel spatial neighborhood. Experiments were carried out on four benchmark hyperspectral datasets, Salinas-A, Kennedy Space Center (KSC), Indian Pines (IP) and Pavia University (Pavia-U). Compared to state-of-the-art models, the proposed network produces promising results for all tested datasets, with an accuracy up to 99.76%.

Different color spaces are better for different applications. This paper investigates the performance of face recognition with some color spaces using kernel-based Principal Component Analysis (Kernel-PCA). Kernel-PCA is a non-linear extension from the popular algorithm PCA. Experiments are performed with the Gaussian kernel function. Color spaces are linear or non-linear transform from RGB. In this paper, the RGB, YCbCr, and HSV color spaces are compared with the gray image (luminance information Y). Kernel-PCA is used to extract features from individual color components or from combining the three components of every color space in one vector. The experiments are performed on FEI color database. FEI database is frontal face images with seven profile images rotation of up to about 180 degrees and two different facial expression images. The experimental results show that the V color component of the HSV color space outperform all the used color organization.