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Advances in Multimedia publishes research on the technologies associated with multimedia systems, including computer-media integration for digital information processing, storage, transmission, and representation.
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Smart Building Skin Design with Dynamic Climate Adaptability of Smart Cities Based on Artificial Intelligence
As the separation and carrier of indoor and outdoor energy and climate conditions, building skin plays an important role in indoor environment regulation and effective utilization of outdoor environmental resources. The traditional fixed skin of residential buildings in cold regions lacks the ability to respond to the external climate, so it is difficult to meet the dual requirements of building energy efficiency and indoor comfort. In the long river of architectural development, the most important thing of architectural design is how to meet the climate adaptability. Traditional architectural forms have long been unable to meet the current social development, climate conditions, and user needs. Based on the basic theory, this paper establishes a systematic understanding of inlay, studies the design method of complex skin with geometric algorithm as the operating tool, discusses the application of this method in architectural design in combination with practice, more systematically and comprehensively studies the building skin with dynamic climate adaptability, and makes a physical model of building skin with dynamic climate adaptability. The contrast experiments under different control modes were carried out using the climate chamber experimental system. This research focuses on taking geometric principles as the prototype, trying to break the common design idea of generating skin by overlapping cells, and providing a systematic skin design method with strong operability and modular structure, hoping to help expand creative thinking.
Analysis of E-Commerce Marketing Strategy Based on Xgboost Algorithm
With the development and popularization of multimedia technology, people’s communication and information dissemination become more and more convenient. The use of new media technology in marketing accelerates the enterprise’s adaptation to market changes. With the help of new marketing technology, the quality of marketing can be improved and the effect of marketing can be maximized. This paper reviews the current literature on e-commerce marketing and then analyzes the feasibility of precision marketing in e-commerce market in the new media era. In order to screen potential consumers and improve the success rate of precision marketing, this paper establishes a prediction model for precision marketing of bank credit cards. A public dataset selected in this paper contains the basic characteristics of the customer, such as age, gender, monthly income, monthly consumption, and whether the customer has agreed to process the credit card after personalized recommendation. After data cleaning and preprocessing, XGboost algorithm is used to predict whether the customer will process the credit card. The calculation shows that the XGboost algorithm can still maintain a high accuracy when dealing with fewer characteristic variables. This study is helpful to better predict users’ consumption intention, further explore potential customers, and improve the success rate of precision marketing.
Privacy Protection of Digital Images Using Watermarking and QR Code-based Visual Cryptography
The increase in information sharing in terms of digital images imposes threats to privacy and personal identity. Digital images can be stolen while in transfer and any kind of alteration can be done very easily. Thus, privacy protection of digital images from attackers becomes very important. Encryption, steganography, watermarking, and visual cryptography techniques to protect digital images have been proposed from time to time. The present paper is focused on the enhancement of privacy protection of digital images utilizing watermarking and a QR code-based expansion-free and meaningful visual cryptography approach which generates visually appealing QR codes for transmitting meaningful shares. The original secret image is processed with a watermark image (copyright logo, signature, and so on), and then halftoning of the watermarked image has been done to limit pixel expansion. Then, the halftoned image has been partitioned using VC into two shares. These shares are embedded with a QR code to make the shares meaningful. Lossless compression has been performed on the QR code-based shares. The compression method employed in visual cryptography would save space and time. The proposed approach keeps the beauty of visual cryptography, i.e., computation-free decryption, and the size of the recovered image the same as the original secret image. The experimental results confirm the effectiveness of the proposed approach.
Image Dehazing Based on Improved Color Channel Transfer and Multiexposure Fusion
Image dehazing is one of the problems that need to be solved urgently in the field of computer vision. In recent years, more and more algorithms have been applied to image dehazing and achieved good results. However, the image after dehazing still has color distortion, contrast and saturation disorder, and other challenges; in order to solve these problems, in this paper, an effective image dehazing method is proposed, which is based on improved color channel transfer and multiexposure image fusion to achieve image dehazing. First, the image is preprocessed using a color channel transfer method based on k-means. Second, gamma correction is introduced on the basis of guided filtering to obtain a series of multiexposure images, and the obtained multiexposure images are fused into a dehazed image through a Laplacian pyramid fusion scheme based on local similarity of adaptive weights. Finally, contrast and saturation corrections are performed on the dehazed image. Experimental verification is carried out on synthetic dehazed images and natural dehazed images, and it is verified that the method proposed is superior to existing dehazed algorithms from both subjective and objective aspects.
FFA-GAN: A Generative Adversarial Network Based on Feature Fusion Attention for Intelligent Safety Monitoring
With the rapid development of the national power grid, there is an increasing and strict demand for accurate intelligent management. However, the current detection algorithms have limited abilities under adverse conditions, especially in regions like Yunnan Province with complex terrain. To address this issue, we propose a method that utilizes infrared and visible images to make the images more informative, thereby improving the accuracy of the detection algorithm for electric power construction site safety. First, we design channel attention (CA) module and pixel attention (PA) module to focus on more important channels and resist thick haze pixels that focus on the thick haze pixels and more important channel information. Furthermore, we design a two-stage discriminator which imposes two restrictions on the fused results. Finally, we conduct a large number of comparison experiments with state-of-the-art methods, and the results show that our proposed fusion method achieves excellent performance in infrared and visible image fusion. This method has good prospects for application in the safety supervision of power construction sites and provides a line of defense for construction workers.
Coordinate Attention Filtering Depth-Feature Guide Cross-Modal Fusion RGB-Depth Salient Object Detection
Existing RGB + depth (RGB-D) salient object detection methods mainly focus on better integrating the cross-modal features of RGB images and depth maps. Many methods use the same feature interaction module to fuse RGB and depth maps, which ignores the inherent properties of different modalities. In contrast to previous methods, this paper proposes a novel RGB-D salient object detection method that uses a depth-feature guide cross-modal fusion module based on the properties of RGB and depth maps. First, a depth-feature guide cross-modal fusion module is designed using coordinate attention to utilize the simple data representation capability of depth maps effectively. Second, a dense decoder guidance module is proposed to recover the spatial details of salient objects. Furthermore, a context-aware content module is proposed to extract rich context information, which can predict multiple objects more completely. Experimental results on six benchmark public datasets demonstrate that, compared with 15 mainstream convolutional neural network detection methods, the saliency map edge contours detected by the proposed model have better continuity and the spatial structure details are clearer. Perfect results are achieved on four quantitative evaluation metrics. Furthermore, the effectiveness of the three proposed modules is verified through ablation experiments.