Open AccessMULTI LABEL CLASSIFICATION FOR AN IMAGE USING CONVOLUTIONAL NEURAL NETWORKS
Author(s) -
N. Lakshmi Prasanna,
R. Vaishnavi,
V. Prasanna Lakshmi,
V. Dakshayani,
T. Keerthana
Publication year - 2021
Publication title -
international journal of computer science and mobile computing
Language(s) - English
Resource type - Journals
ISSN - 2320-088X
DOI - 10.47760/ijcsmc.2021.v10i07.001
Subject(s) - multi label classification , computer science , contextual image classification , pattern recognition (psychology) , one class classification , artificial intelligence , convolutional neural network , binary classification , class (philosophy) , multiclass classification , domain (mathematical analysis) , image (mathematics) , machine learning , statistical classification , support vector machine , mathematics , mathematical analysis
The machine learning has many capabilities one of them is classification. Classification employed in many contexts like telling hotel reviews good / bad, or inferring the image consists of dog, cat etc. As the data increases there is a need to organize it, for that purpose classification can be helpful. Modern classification problems involve the prediction of multiple labels simultaneously associated with a single instance known as "Multi Label Classification". In multi-label classification, each of the input data samples belongs to one or more than one classes or labels. The traditional binary and multi-class classification problems are the subset of the multi-label classification problem. In this paper we are implementing the multi label classification using CNN framework with keras libraries. Classification can be applied to different domain such as text, audio etc. In this paper we are applying classification for an image dataset.