Open AccessArtificial Neural Network (ANN) based Object Recognition Using Multiple Feature Sets
Author(s) -
Manami Barthakur,
Tapashi Thakuria,
Kandarpa Kumar Sarma
Publication year - 2011
Publication title -
international journal of electronic signal and systems
Language(s) - English
Resource type - Journals
ISSN - 2231-5969
DOI - 10.47893/ijess.2011.1007
Subject(s) - artificial intelligence , artificial neural network , pattern recognition (psychology) , computer science , discrete cosine transform , principal component analysis , feature (linguistics) , perceptron , object (grammar) , multilayer perceptron , backpropagation , time delay neural network , feature extraction , set (abstract data type) , domain (mathematical analysis) , cognitive neuroscience of visual object recognition , component (thermodynamics) , image (mathematics) , mathematics , mathematical analysis , philosophy , linguistics , physics , thermodynamics , programming language
In this work, a simplified Artificial Neural Network (ANN) based approach for recognition of various objects is explored using multiple features. The objective is to configure and train an ANN to be capable of recognizing an object using a feature set formed by Principal Component Analysis (PCA), Frequency Domain and Discrete Cosine Transform (DCT) components. The idea is to use these varied components to form a unique hybrid feature set so as to capture relevant details of objects for recognition using a ANN which for the work is a Multi Layer Perceptron (MLP) trained with (error) Back Propagation learning.