Open AccessDeep Fuzzy Multi-Object Categorization in Scene
Author(s) -
S.Kumaravel,
S. Veni
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.a8177.1110120
Subject(s) - categorization , artificial intelligence , computer science , fuzzy logic , fuzzy set , thresholding , pattern recognition (psychology) , fuzzy classification , convolutional neural network , entropy (arrow of time) , data mining , word error rate , defuzzification , object (grammar) , machine learning , fuzzy number , image (mathematics) , physics , quantum mechanics
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.