Artificial Intelligent Systems and Machine Learning

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Deep Learning based CNN Architecture „NavNet‟ For the Identification of Object Class and Qualities in Real Images .


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Identification of object in an image is the important issue in computer vision applications and afterward-subsequent task is to categorize these objects in suitable classes. The majority of the visualization work fundamentally based on the ability to identify objects, pictures, and subsequently categorize. Computer vision categorization itself has a variety of probable applications with the purpose to cover up many fields of machine learning and data mining and a range of applications for instance content-based image retrieval, video image retrieval, or object identification for movable robots etc. I have tried many techniques to investigate optimum method to improve the convolutional neural network based image recognition and classification of given object. Here I present a simple, easy yet strong formulation of object identification and classification as a regression problem to object bounding box, and define a multi-scale inference procedure, which is able to produce object identification and classification with high resolution at a low cost by network applications. This method is based on deep learning convolution neural network and now becoming the state of the art technique in objects classification and image processing.

Keywords

Object Classification, Deep Learning, Regression, CNN.
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  • Deep Learning based CNN Architecture „NavNet‟ For the Identification of Object Class and Qualities in Real Images .

Abstract Views: 156  |  PDF Views: 5

Authors

Dr. N. K. Darwante
no abstract, India

Abstract


Identification of object in an image is the important issue in computer vision applications and afterward-subsequent task is to categorize these objects in suitable classes. The majority of the visualization work fundamentally based on the ability to identify objects, pictures, and subsequently categorize. Computer vision categorization itself has a variety of probable applications with the purpose to cover up many fields of machine learning and data mining and a range of applications for instance content-based image retrieval, video image retrieval, or object identification for movable robots etc. I have tried many techniques to investigate optimum method to improve the convolutional neural network based image recognition and classification of given object. Here I present a simple, easy yet strong formulation of object identification and classification as a regression problem to object bounding box, and define a multi-scale inference procedure, which is able to produce object identification and classification with high resolution at a low cost by network applications. This method is based on deep learning convolution neural network and now becoming the state of the art technique in objects classification and image processing.

Keywords


Object Classification, Deep Learning, Regression, CNN.

References