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Different materials used in a home (such as wood, metal and glass.) will affect the presentation of color, which may cause color deviation or change. At the same time, the color of some household products will change or fade after a period of use, which may lead to the original design color effect no longer meeting the expectations. Therefore, in order to ensure the accuracy of color matching and meet the needs of color matching in home design, a color design method in home field based on computer cloud image processing technology is proposed. First, the method collects and preprocesses the images in the home field, and stores the collected images on the cloud platform. Then, the improved non-mean filtering algorithm is used to denoise the home domain image downloaded from the cloud platform. Then, white balance preprocessing is performed on the home image with color deviation by using the color deviation detection method of color distortion factor to ensure color accuracy. Finally, the contrast enhanced home image is converted to HSV color space, and saturation compensation is carried out in the channel to complete the color design in the home field. The experimental results show that this method has lower packet loss rate, better denoising effect and color balance in the cloud transmission of home image.

In order to significantly reduce the computational complexity to achieve efficient pose estimation of yoga pose practice, this paper studies the pose estimation method of fitness yoga pose practice based on lightweight OpenPose. First, based on the video image of fitness yoga postures, the background model of the video image is constructed through HSV color space conversion processing. The background model is used to segment the foreground and background of yoga pose practice. A lightweight OpenPose fitness yoga pose training pose estimation model is established. This model uses a 10-layer VGG19 network to deeply study the segmented yoga pose foreground image, so as to extract the feature map of yoga pose training. These feature maps are then input into a multi-stage, two-branch convolutional neural network to further calculate the joint heat map and joint affinity domain map of fitness yoga pose exercises. In order to capture and track the joints of human motion in real time, the pose distance between frames and the maximum weight matching algorithm of a bipartite graph is used to track the pose of continuous frames of video images. Through the least squares support vector machine classifier integrated into the softmax layer of the lightweight OpenPose network model, the final pose estimation results of fitness yoga exercises are output. The experiment shows that this method can effectively realize the color space conversion from RGB to HSV of the fitness yoga pose exercise action image, and can realize the foreground and background segmentation of the fitness yoga pose exercise action image. At the same time, it can accurately identify the yoga pose exercise action posture Corresponding to different frames in a fitness yoga pose exercise action video, and the application effect is better.

This paper proposes a Content-Based Image Retrieval (CBIR) system applicable in mobile devices. Due to the fact that different queries to a content-based image retrieval (CBIR) system emphasize different subsets of a large collection of features, most CBIR systems using only a few features are therefore only suitable for retrieving certain types of images. In this research we combine a wide range of features, including edge information, texture energy, and the HSV color distributions, forming a feature space of up to 1053 dimensions, in which the system can search for features most desired by the user. Through a training process using the AdaBoost algorithm⁹ our system can efficiently search for important features in a large set of features, as indicated by the user, and effectively retrieve the images according to these features. The characteristics of the system meet the requirements of mobile devices for performing image retrieval. The experimental results show that the performance of the proposed system is sufficiently applicable for mobile devices to retrieve images from a huge database.

Human visual perception has a close relationship with the HSV color space, which can be represented as a cylinder. The question of how visual features are extracted using such an attribute is important. In this paper, a new feature descriptor; namely, a color volume histogram, is proposed for image representation and content-based image retrieval. It converts a color image from RGB color space to HSV color space and then uniformly quantizes it into 72 bins of color cues and 32 bins of edge cues. Finally, color volumes are used to represent the image content. The proposed algorithm is extensively tested on two Corel datasets containing 15000 natural images. These image retrieval experiments show that the color volume histogram has the power to describe color, texture, shape and spatial features and performs significantly better than the local binary pattern histogram and multi-texton histogram approaches.

Multiple images of a scene, captured using different imaging devices and containing complementary information, are required to be combined into a single fused image. The fused image, so obtained, should ideally contain all important features of individual input images. Further, color image processing is also gaining importance in most of the real-life applications. Color images provide better visual information and are highly suitable for human visual perception. Motivated by the gaining importance of color image processing, fusion of color images is proposed. The fusion algorithm is proposed in HSV color space where the luminance components of multiple input images, based on fuzzy transform and spatial frequency, are fused. The chrominance information of input images provides color to the fused image. The fused image contains all important information and possesses natural color appearance. Experiments performed on various pairs of visible–infrared and multifocus images demonstrate the superiority of the proposed algorithm over recent state-of-the-art image fusion algorithms.