用于分类和内容检索的高压缩图像表示法

IF 5.8 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Stanisław Łażewski, Bogusław Cyganek
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引用次数: 0

摘要

摘要 在本文中,我们提出了一种使用高度压缩特征表示图像的新方法,用于分类和图像内容检索--称为 PCA-ResFeats。这些特征是通过融合 ResNet-50 残差块输出中的高层和低层特征并对其进行主成分分析而得到的,从而显著降低了维度。此外,通过应用浮点压缩,我们能够将存储单张图像所需的内存减少到 jpg 图像的 1,200 倍,比 ResNet-50 的简单输出融合特征减少 220 倍。因此,数据集中单张图像的平均表示量可低至 35 字节。与通过融合最后一个 ResNet-50 剩余块获得的特征进行分类的结果相比,我们获得了相当高的准确率(不低于五个百分点),同时保持了两个数量级的数据压缩。我们还在基于内容的图像检索任务中测试了我们的方法,结果优于其他使用稀疏特征的已知方法。此外,我们的方法还能创建图像内容的简明摘要,这在数据库中应用广泛。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Highly compressed image representation for classification and content retrieval

Abstract

In this paper, we propose a new method of representing images using highly compressed features for classification and image content retrieval – called PCA-ResFeats. They are obtained by fusing high- and low-level features from the outputs of ResNet-50 residual blocks and applying to them principal component analysis, which leads to a significant reduction in dimensionality. Further on, by applying a floating-point compression, we are able to reduce the memory required to store a single image by up to 1,200 times compared to jpg images and 220 times compared to features obtained by simple output fusion of ResNet-50. As a result, the representation of a single image from the dataset can be as low as 35 bytes on average. In comparison with the classification results on features from fusion of the last ResNet-50 residual block, we achieve a comparable accuracy (no worse than five percentage points), while preserving two orders of magnitude data compression. We also tested our method in the content-based image retrieval task, achieving better results than other known methods using sparse features. Moreover, our method enables the creation of concise summaries of image content, which can find numerous applications in databases.

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来源期刊
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering 工程技术-工程:综合
CiteScore
9.90
自引率
21.50%
发文量
21
审稿时长
>12 weeks
期刊介绍: Integrated Computer-Aided Engineering (ICAE) was founded in 1993. "Based on the premise that interdisciplinary thinking and synergistic collaboration of disciplines can solve complex problems, open new frontiers, and lead to true innovations and breakthroughs, the cornerstone of industrial competitiveness and advancement of the society" as noted in the inaugural issue of the journal. The focus of ICAE is the integration of leading edge and emerging computer and information technologies for innovative solution of engineering problems. The journal fosters interdisciplinary research and presents a unique forum for innovative computer-aided engineering. It also publishes novel industrial applications of CAE, thus helping to bring new computational paradigms from research labs and classrooms to reality. Areas covered by the journal include (but are not limited to) artificial intelligence, advanced signal processing, biologically inspired computing, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, intelligent and adaptive systems, internet-based technologies, knowledge discovery and engineering, machine learning, mechatronics, mobile computing, multimedia technologies, networking, neural network computing, object-oriented systems, optimization and search, parallel processing, robotics virtual reality, and visualization techniques.
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