Object Classification has received wide attention due to its real time applications using deep learning technique. It is a process of classifying objects into predefined and semantically meaningful categories. Due to less accuracy ,sometimes gives inaccurate result and wouldn’t be effective. Object Classification using CNN algorithm is a proposed system in which image is inserted and object is classified and displayed in UI based on trained model.Inthis,it extracts features from the image, undergoes three architectures and high accuracy output will be considered to overcome existing difficulties. Objects can be easily detected and identified by humans. The visual system of human is very fast and accurate and can perform complex tasks like object identification and detection very easily .Situation like where we have to find from so many things and trying to find that key will take long time and we have to face some difficulties. Object classification is very important for applications in automatic visual surveillance system. The process of classifying objects into predefined and semantically meaningful categories using its features is called object classification. In this paper we propose a new model for detection and classification of objects by taking the features to classify the detected objects using Deep Neural Network (DNN). Deep Neural Networks are capable of handling large higher dimensional data with billions of parameters as like human brain. Simulation results obtained illustrate that the proposed classifier model produces more accurate results for feature extraction and DNN for classification.
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