Deep Learning Machine using Hierarchical Cluster Features


  • Sara Salman Department of Computer Science, College of Science, Mustansiriyah University, IRAQ.
  • Jamila H. Soud Department of Computer Science , College of Science, University of Baghdad, IRAQ.



Scale Invariant Feature Transform (SIFT), Probabilistic Neural Networks (PNN), Hierarchical Cluster.


Deep learning of multi-layer computational models allowed processing to recognize data representation at multiple levels of abstraction. These techniques have greatly improved the latest ear recognition technology. PNN is a type of radiative basis for classification problems and is based on the Bayes decision-making base, which reduces the expected error of classification. In this paper, strong features of images are used to give a good result, therefore, SIFT method using these features after adding improvements and developments. This method was one of the powerful algorithms in matching that needed to find energy pixels. This method gives stronger feature on features and gives a large number of a strong pixel, which is considered a center and neglected the remainder of it in our work. Each pixel of which is constant for image translation, scaling, rotation, and embedded lighting changes in lighting or 3D projection. Therefore, the interpretation is developed by using a hierarchical cluster method; to assign a set of properties (find the approximation between pixels) were classified into one.


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Key Dates



How to Cite

S. Salman and J. H. Soud, “Deep Learning Machine using Hierarchical Cluster Features”, Al-Mustansiriyah Journal of Science, vol. 29, no. 3, pp. 82–93, Mar. 2019, doi: 10.23851/mjs.v29i3.625.

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