According to the fast development of Internet technologies, nowadays more and more web applications require accessing and processing massive-scale visual data, such as content image search and automatic navigation. Practically, the computing power of a single personal PC is insufficient for handling such massive-scale visual elements. Owing to the advancement of cloud platforms, this problem can be well handled. In this work, we study the problem of how to optimally distribute the P visual processing tasks to Q computing resources. Specifically, we proposed an enhanced multimodal cat swarm optimization (MCSO) algorithm to fulfill this task. Given a huge number of images/videos, we first extract color, texture, and semantic channel to represent the visual content of each image/video. Afterward, we develop a multi-view feature learning algorithm to intelligently combine the multiple features into a descriptive one, wherein the weights of different feature channels are adjusted automatically. Subsequently, we use the CSO algorithm to assign each image/video to different remote servers. The CSO algorithm mimics the conventional cat hunting process, that is, a set of cats is divided into two groups, one for searching and the other for tracking. The two patterns are interacted by the mixture ratio, which indicates how many cats will conduct tracking in the next round. Based on the output of CSO, each image/video will be assigned to the most optimal remote sensor. Finally, based on the optimal assignment, the image/video processing can be conducted with the minimal time consumption. Comprehensive experimental comparisons have demonstrated the advantages of our method, that is, our MCSO can achieve a visual processing time three times faster than the second best performer.

根据Internet技术的飞速发展，如今，越来越多的Web应用程序需要访问和处理大规模的可视数据，例如内容图像搜索和自动导航。实际上，单个个人PC的计算能力不足以处理如此大规模的视觉元素。由于云平台的发展，可以很好地解决此问题。在这项工作中，我们研究如何将P视觉处理任务最佳地分配给Q计算资源的问题。具体来说，我们提出了一种增强的多峰猫群优化（MCSO）算法来完成此任务。给定大量的图像/视频，我们首先提取颜色，纹理和语义通道来表示每个图像/视频的视觉内容。之后，我们开发了一种多视图特征学习算法，将多个特征智能地组合成一个描述性特征，其中不同特征通道的权重会自动调整。随后，我们使用CSO算法将每个图像/视频分配给不同的远程服务器。CSO算法模仿了传统的猎猫过程，即，将一组猫分为两组，一组用于搜索，另一组用于跟踪。这两种模式通过混合比例相互影响，该比例表示下一轮将进行追踪的猫数量。根据CSO的输出，每个图像/视频将分配给最佳的遥感器。最后，基于最佳分配，可以以最少的时间消耗进行图像/视频处理。