Open AccessTowards Describing Visual Explanation using Machine Learning
Author(s) -
Miss.D.G. Wadnere*,
Mr. Naresh Chandramouli Thotam
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.l3454.129219
Subject(s) - computer science , set (abstract data type) , replicate , class (philosophy) , a priori and a posteriori , artificial intelligence , point (geometry) , markov chain , machine learning , image (mathematics) , perspective (graphical) , hidden markov model , cache , mathematics , programming language , philosophy , statistics , geometry , epistemology , operating system
Current available visible explanation generating systems research to easily absolve a class prediction. Still, they may additionally point out visible parameters attribute which replicate a strong category prior, though the proof may additionally not clearly be in the pic. This is specifically regarding as alternatively such marketers fail in constructing have confidence with human users. We proposed our own version, which makes a speciality of the special places of house of the seen item, together predicts the category label & interprets why the expected label is proper for the image. The machine proposes to annotate the images automatically using the Markov cache model. To annotate images, principles are represented as states through the usage of Hidden Markov model. The model parameters were estimated as part of a set of images and manual annotations. This is a great collection of checks, albeit automatically, with the possibility a posteriori of the concepts presented in her.