Deep Fuzzy Multi-Object Categorization in Scene
1S.Kumaravel, Associate Prof. in Computer Science, SRMV College of Arts and Science, Coimbatore, (T.N), India.
2S.Veni, Prof. & HOD, Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore, (T.N), India.
Manuscript received on September 20, 2020. | Revised Manuscript received on November 01, 2020. | Manuscript published on November 10, 2021. | PP: 262-267 | Volume-10 Issue-1, November 2020 | Retrieval Number: 100.1/ijitee.A81771110120| DOI: 10.35940/ijitee.A8177.1110120
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Object Categorization is the process of, identifying and labelling the various distinct Classes (Categories), in the given input image. The Deep Fuzzy Multi-Object Categorization (DFMOC) model, combines the learning capability of Convolution Neural Networks (CNN) and the uncertainty-managing ability of Fuzzy system, for carrying out the categorization task. This work starts with Background Elimination process for ensuring the image clarity, followed by Fuzzification and Fuzzy Entropy computation. Simple fuzzy sets are to be framed, by employing Fuzzy C-Means (FCM) algorithm, for fuzzification of the input image. Thresholding Block is incorporated, for determining the clusters . The Fuzzy Entropy Computation (FEC) is done, to minimize the Fuzziness rate of the acquired input and consequently, the layers of CNN are trained in accordance with that. Caltech-101 Dataset is been utilized for analysis. Average Precision Rate of Categorization (APRC), along with other metrics namely Time taken and Error Rate, shows that DFMOC model performs better than other models.
Keywords: Deep Fuzzy Model, Fuzzy Entropy, Object Categorization, Thresholding.
Scope of the Article: Fuzzy Logics