Open AccessObject Identification Model using Deep Reinforcement Machine Learning Concept for Image
Author(s) -
Saurabh Tiwari,
S. Veenadhari,
Sanjeev Kumar Gupta
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1085/1/012024
Subject(s) - softmax function , computer science , reinforcement learning , pascal (unit) , artificial intelligence , identification (biology) , object (grammar) , machine learning , cognitive neuroscience of visual object recognition , process (computing) , object detection , pattern recognition (psychology) , contextual image classification , class (philosophy) , image (mathematics) , computer vision , deep learning , operating system , botany , biology , programming language
This paper presents a model which gives the detailed process of object identification. We need to identify class and location of object in image for completing process of objet identification. Proposed model works on the principal of reinforcement learning which takes action on the basis of rewards and experiences. Normally methods in literature uses sliding window which moves in same direction but proposed algorithm provides a variable mask which moves 360 degree for identifying object using action history vector proposed with RL also not only this work focuses on localization like other work but also used class information with Softmax classification able to classify multiple object in single image with efficient time which is novel. Proposed mask acts as agent and focuses on proposed candidate reason this saves time and works in efficient manner for identification. Agent depends on transformation action and by applying top down reasoning it gives location of object. Classification is done using Softmax classification as we are having features of image by CNN. Reinforcement learning concept used for training of agent and Pascal voc dataset used for testing. Analysis of only 10 to 25 regions is sufficient with proposed work to identify first instance of object. Experiment and performance evaluation shows the efficiency of proposed work.