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Advances in Signal Processing and Intelligent Recognition Systems pp 257–265Cite as

An Efficient Multi Object Image Retrieval System Using Multiple Features and SVM

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 425))

Abstract

Retrieval of images containing multiple objects has been an active research area in recent years. This paper presents an efficient retrieval system for images containing multiple objects using shape, texture, edge histogram features and SVM (Support Vector Machine). The process starts with preparing the knowledge database. In this, all the images in dataset are segmented and shape, texture, edge histogram features are extracted. A combined feature vector is generated by combining these multiple features. These features are trained by using SVM and all the images in database are classified into different classes. This information is stored as knowledge data base. When the user enters his choice of query image, it is segmented and features are extracted. The combined feature vector is generated by using all these features. SVM is used for matching of the query image feature vector class with the classes in knowledge database. The similarity distance is calculated between the feature vector of the query image and the images in the corresponding class. The images are sorted and displayed according to the ascending order of the similarity distance. Experimental results demonstrate better retrieval efficiency.

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Author information

Authors and Affiliations

  1. Department of Electronics and Communications, Jain University, Bangalore, Karnataka, India

    Nizampatnam Neelima

  2. Department of Computer Science and Engineering, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India

    E. Sreenivasa Reddy

Authors
  1. Nizampatnam Neelima
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  2. E. Sreenivasa Reddy
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Corresponding author

Correspondence to Nizampatnam Neelima .

Editor information

Editors and Affiliations

  1. and Management – Kerala (IIITM-K), Indian Inst. of Information Technology, Trivandrum, Kerala, India

    Sabu M. Thampi

  2. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Sanghamitra Bandyopadhyay

  3. Department of Electrical, Ryerson University, Toronto, Ontario, Canada

    Sri Krishnan

  4. Providence University, Taichung, Taiwan

    Kuan-Ching Li

  5. Computer Engineering Department, Vladimir State University, Vladimir Region, Russia

    Sergey Mosin

  6. School of Electrical, Nanyang Technological University, Singapore, Singapore

    Maode Ma

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© 2016 Springer International Publishing Switzerland

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Neelima, N., Reddy, E.S. (2016). An Efficient Multi Object Image Retrieval System Using Multiple Features and SVM. In: Thampi, S., Bandyopadhyay, S., Krishnan, S., Li, KC., Mosin, S., Ma, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. Advances in Intelligent Systems and Computing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-319-28658-7_22

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  • DOI: https://doi.org/10.1007/978-3-319-28658-7_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28656-3

  • Online ISBN: 978-3-319-28658-7

  • eBook Packages: EngineeringEngineering (R0)

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