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Visual computing resources distribution and balancing by multimodal cat swarm optimization
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AI
DOI：10.1016/j.image.2020.115816.png)
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摘要
En 中文
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 mull-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.
Keyword:
Visual computing
Resources distribution
Multi-view feature fusion
Cat swarm optimization
Mixture ratio
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2.7
论文数:
2.8K
被引数:
4.2K
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