title: Geometry-invariant texture retrieval using a dual-output pulse-coupled neural network.
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.
authors.affiliation: School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province 730000, China. xjlilzu@hotmail.com
authors.forename: Xiaojun
authors.initials: X
authors.lastname: Li
authors.forename: Yide
authors.initials: Y
authors.lastname: Ma
authors.forename: Zhaobin
authors.initials: Z
authors.lastname: Wang
authors.forename: Wenrui
authors.initials: W
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D016247:Information Storage and Retrieval; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D012815:Signal Processing, Computer-Assisted