Image Classification Using CNN

Abstract

Content Based Image Retrieval Technique(CBIR) is used to retrieve images from a database by adding some algorithms. The images are initially stored in the database and then retrieved on the basis of different features and techniques. User can extract images based on different search results. Still, there are various algorithms which are unable to find some specific criteria. Users directly write any name and get relevant results based on that. But there were lots of challenges which were solved by using various algorithms. The algorithms used in CBIR must be optimized for good results as well as higher accuracy and recall rate. Image classification is a technique in which the images are classified into different classes. Image classification is used to accurately classify the images based on different categories and based on different techniques the images are been set to a particular class. If an image belongs to the class A, then the algorithm must ensure that it must classify it as class A image. Convolutional neural network(CNN) is a technique which we can use for the image classification. This paper will show how the image classification works in case of cifar-10 dataset. We used the sequential method for the CNN and implemented the program in jupyter notebook. We took 3 classes and classify them using CNN. The classes were aeroplane, bird and car.We presente d the classification by using CNN and we took batch size as 64. We got 94% accuracy for the 3 classes used in cifar-10 dataset.