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Learning Semantic Features for Classifying Very Large Image Datasets Using Convolution Neural Network

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Abstract

Advancements in sensors and image acquisition devices lead to tremendous increase in creation of unlabeled database of images, and traditional image retrieval approaches are inefficient in retrieving semantic images. Neural network is emerging as popular method to solve most of the state-of-the-art problems in filling the gap between low level features and high level semantics. Convolution neural network (CNN) is a category of neural network which automatically extracts the important features without any human intervention, with considerably reduced set of parameters. In this paper, a model is generated using CNN (VGG-16) architecture which combines convolution and max pooling layers at different levels using effective regularization and transfer learning with data augmentation. The effectiveness of proposed model is demonstrated on benchmarking Caltech 101 and 256 datasets. The results obtained have proven the capacity of model in understanding high level semantics and outperformed several contemporary techniques.

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  1. Department of ECE, SJB Institute of Technology, Bangalore, India

    A. Shubha Rao & K. Mahantesh

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This article is part of the topical collection “Data Science and Communication” guest edited by Kamesh Namudri, Naveen Chilamkurti, Sushma S J and S. Padmashree.

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Rao, A.S., Mahantesh, K. Learning Semantic Features for Classifying Very Large Image Datasets Using Convolution Neural Network. SN COMPUT. SCI. 2, 187 (2021). https://doi.org/10.1007/s42979-021-00589-6

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