Geometry-Invariant Texture Retrieval Using a Dual-Output Pulse-Coupled Neural Network

Author:

Li Xiaojun1,Ma Yide1,Wang Zhaobin1,Yu Wenrui1

Affiliation:

1. School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province 730000, China

Abstract

This letter proposes a novel dual-output pulse coupled neural network model (DPCNN). The new model is applied to obtain a more stable texture description in the face of the geometric transformation. Time series, which are computed from output binary images of DPCNN, are employed as translation-, rotation-, scale-, and distortion-invariant texture features. In the experiments, DPCNN has been well tested by using Brodatz's album and the VisTex database. Several existing models are compared with the proposed DPCNN model. The experimental results, based on different testing data sets for images with different translations, orientations, scales, and affine transformations, show that our proposed model outperforms existing models in geometry-invariant texture retrieval. Furthermore, the robustness of DPCNN to noisy data is examined in the experiments.

Publisher

MIT Press - Journals

Subject

Cognitive Neuroscience,Arts and Humanities (miscellaneous)

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Leaf Image Recognition Based on Bag of Features;Applied Sciences;2020-07-28

2. Analysis of pulse period for passive neuron in pulse coupled neural network;Mathematics and Computers in Simulation;2019-01

3. Taking advantage of motif matrix inference for rotated image indexing and retrieval;EURASIP Journal on Advances in Signal Processing;2018-10-01

4. Pulse Coupled Neural Network based Near-Duplicate Detection of Images (PCNN - NDD);Advances in Electrical and Computer Engineering;2018

5. Leaf Recognition Based on DPCNN and BOW;Neural Processing Letters;2017-05-20

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