Internet of things image recognition system based on deep learning

IF 1.5 Q2 COMPUTER SCIENCE, THEORY & METHODS
Jing Li, Xinfang Li, Yuwen Ning
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引用次数: 2

Abstract

At present, many exciting results have been achieved in the application of deep learning to image recognition. However, there are still many problems to be overcome before deep learning is used in practical applications such as image retrieval, image annotation, and image-text conversion. This paper studies the structure of deep learning, improves the commonly used training algorithms, and proposes two new neural network models for different application scenarios. This paper uses Support Vector Machine (SVM) as the main classifier for Internet of Things image recognition and uses the database of this paper to train SVM and CNN. At the same time, the effectiveness of the two for image recognition is tested, and the trained classifier is used for image recognition. The result surface: In the labeled data set, the rank-1 accuracy of CNN is 85.77%, which is higher than 90.28% of the SVM method. In the detection data, CNN’s rank-1 accuracy rate is 83.11%, which also exceeds SVM’s 80.22%. SVM+CNN has a rank 1 value of 84.69% for the detection data set. This shows that deep learning can map the feature representation of the image and the feature representation of the word to the same space, making the calculation of the similarity and correlation between the image and the text easier and more straightforward.
基于深度学习的物联网图像识别系统
目前,深度学习在图像识别中的应用已经取得了许多令人兴奋的成果。然而,深度学习在实际应用中仍有许多问题需要克服,如图像检索、图像标注、图像-文本转换等。本文研究了深度学习的结构,改进了常用的训练算法,针对不同的应用场景提出了两种新的神经网络模型。本文采用支持向量机(SVM)作为物联网图像识别的主要分类器,并利用本文的数据库对SVM和CNN进行训练。同时,测试了两者用于图像识别的有效性,并将训练好的分类器用于图像识别。结果面:在标注数据集中,CNN的rank-1准确率为85.77%,高于SVM方法的90.28%。在检测数据中,CNN的rank-1准确率为83.11%,也超过了SVM的80.22%。SVM+CNN对于检测数据集的rank 1值为84.69%。这表明,深度学习可以将图像的特征表示和单词的特征表示映射到同一空间,使得图像和文本之间的相似度和相关性的计算更加简单和直接。
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来源期刊
CiteScore
2.80
自引率
23.10%
发文量
31
期刊介绍: The International Journal of Fuzzy Logic and Intelligent Systems (pISSN 1598-2645, eISSN 2093-744X) is published quarterly by the Korean Institute of Intelligent Systems. The official title of the journal is International Journal of Fuzzy Logic and Intelligent Systems and the abbreviated title is Int. J. Fuzzy Log. Intell. Syst. Some, or all, of the articles in the journal are indexed in SCOPUS, Korea Citation Index (KCI), DOI/CrossrRef, DBLP, and Google Scholar. The journal was launched in 2001 and dedicated to the dissemination of well-defined theoretical and empirical studies results that have a potential impact on the realization of intelligent systems based on fuzzy logic and intelligent systems theory. Specific topics include, but are not limited to: a) computational intelligence techniques including fuzzy logic systems, neural networks and evolutionary computation; b) intelligent control, instrumentation and robotics; c) adaptive signal and multimedia processing; d) intelligent information processing including pattern recognition and information processing; e) machine learning and smart systems including data mining and intelligent service practices; f) fuzzy theory and its applications.
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