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A Study: Multiple-Label Image Classification Using Deep Convolutional Neural Network Architectures

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Artificial Intelligence and Evolutionary Computations in Engineering Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1056))

Abstract

Multiple-label image classification is a kind of supervised learning approach, in which each image can belong to the set of multiple classes. The area of multiple-label image classification increases in recent years, in the field of machine learning. Multiple-label image classification has created significant scope from research people and it has been employed in an image labeling and object prediction tasks. Many conventional machine learning algorithms used for multiple-label image prediction by using relationship between objects in the image, co-occurrence objects, and rank between multiple instances in an image. Deep ConvNet architectures have proved increasing classification performance in image object classification in recent time. This study presents the task of multiple-label classification, the various literatures in area of multiple-label image classification using deep ConvNet architecture, also provides evaluation measures and performs a comparative analysis of the architecture models on PASCAL VOC dataset and various future challenges in the multiple-label image area.

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Authors and Affiliations

  1. Department of Software Engineering, SRM Institute of Science and Technology, Chennai, India

    S. Joseph James & C. Lakshmi

Authors
  1. S. Joseph James
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  2. C. Lakshmi
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Corresponding author

Correspondence to S. Joseph James .

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Editors and Affiliations

  1. Department of Electrical Engineering, Government College of Engineering, Keonjhar, India

    Subhransu Sekhar Dash

  2. Department of Software Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India

    C. Lakshmi

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  4. Department of Electrical Engineering, IIT Delhi, New Delhi, Delhi, India

    Bijaya Ketan Panigrahi

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James, S.J., Lakshmi, C. (2020). A Study: Multiple-Label Image Classification Using Deep Convolutional Neural Network Architectures. In: Dash, S., Lakshmi, C., Das, S., Panigrahi, B. (eds) Artificial Intelligence and Evolutionary Computations in Engineering Systems. Advances in Intelligent Systems and Computing, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-15-0199-9_65

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