Hyperbolic Hopfield neural networks for image classification in content-based image retrieval

被引:15
|
作者
Anitha, K. [1 ]
Dhanalakshmi, R. [2 ]
Naresh, K. [3 ]
Devi, D. Rukmani [4 ]
机构
[1] SIMATS, Saveetha Sch Engn, Chennai, Tamil Nadu, India
[2] Anna Univ, RMK Engn Coll, Chennai, Tamil Nadu, India
[3] VIT Univ, Vellore, Tamil Nadu, India
[4] Anna Univ, RMD Engn Coll, Chennai, Tamil Nadu, India
关键词
Content-based image retrieval; machine learning; hyperbolic valued Hopfield neural network; image classifiers; pattern recognition; SYSTEM;
D O I
10.1142/S0219691320500599
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Neural networks play a significant role in data classification. Complex-valued Hopfield Neural Network (CHNN) is mostly used in various fields including the image classification. Though CHNN has proven its credibility in the classification task, it has a few issues. Activation function of complex-valued neuron maps to a unit circle in the complex plane affecting the resolution factor, flexibility and compatibility to changes, during adaptation in retrieval systems. The proposed work demonstrates Content-Based Image Retrieval System (CBIR) with Hyperbolic Hopfield Neural Networks (HHNN), an analogue of CHNN for classifying images. Activation function of the Hyperbolic neuron is not cyclic in hyperbolic plane. The images are mathematically represented and indexed using the six basic features. The proposed HHNN classifier is trained, tested and evaluated through extensive experiments considering individual features and four combined features for indexing. The obtained results prove that HHNN guides retrieval process, enhances system performance and minimizes the cost of implementing Neural Network Classifier-based image retrieval system.
引用
收藏
页数:39
相关论文
共 50 条
  • [1] Content-based Image Retrieval using Perceptual Image Hashing and Hopfield Neural Network
    Sabahi, F.
    Ahmad, M. Omair
    Swamy, M. N. S.
    2018 IEEE 61ST INTERNATIONAL MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS (MWSCAS), 2018, : 352 - 355
  • [2] Interactive Content-Based Image Retrieval with Deep Neural Networks
    Pyykko, Joel
    Glowacka, Dorota
    SYMBIOTIC INTERACTION (SYMBIOTIC 2016), 2017, 9961 : 77 - 88
  • [3] Content-Based Image Retrieval using Convolutional Neural Networks
    Rian, Zakhayu
    Christanti, Viny
    Hendryli, Janson
    2019 IEEE INTERNATIONAL CONFERENCE ON SIGNALS AND SYSTEMS (ICSIGSYS), 2019, : 1 - 7
  • [4] Content-based image categorization and retrieval using neural networks
    Zhu, Yuhua
    Liu, Xiuwen
    Mio, Washington
    2007 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-5, 2007, : 528 - 531
  • [5] Query Classification in Content-Based Image Retrieval
    Markov, Ilya
    Vassilieva, Natalia
    DATABASES AND INFORMATION SYSTEMS V, 2009, 187 : 281 - +
  • [6] Texture classification for content-based image retrieval
    Pirrone, R
    La Cascia, M
    11TH INTERNATIONAL CONFERENCE ON IMAGE ANALYSIS AND PROCESSING, PROCEEDINGS, 2001, : 398 - 403
  • [7] A classification framework for content-based image retrieval
    Aksoy, S
    Haralick, RM
    16TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL II, PROCEEDINGS, 2002, : 503 - 506
  • [8] Scene Classification for Content-Based Image Retrieval
    Cavus, Oezge
    Aksoy, Selim
    2008 IEEE 16TH SIGNAL PROCESSING, COMMUNICATION AND APPLICATIONS CONFERENCE, VOLS 1 AND 2, 2008, : 753 - 756
  • [9] An Unsupervised Learning Based Method for Content-based Image Retrieval using Hopfield Neural Network
    Sabahi, F.
    Ahmad, M. Omair
    Swamy, M. N. S.
    2016 2ND INTERNATIONAL CONFERENCE OF SIGNAL PROCESSING AND INTELLIGENT SYSTEMS (ICSPIS), 2016, : 76 - 80
  • [10] NNk networks for content-based image retrieval
    Heesch, D
    Rüger, S
    ADVANCES IN INFORMATION RETRIEVAL, PROCEEDINGS, 2004, 2997 : 253 - 266