Adaptive bag-of-visual word modelling using stacked-autoencoder and particle swarm optimisation for the unsupervised categorisation of images

Authors

Abass Olaode, University of WollongongFollow
Golshah Naghdy, University of WollongongFollow

RIS ID

145129

Publication Details

A. Olaode & G. Naghdy, "Adaptive bag-of-visual word modelling using stacked-autoencoder and particle swarm optimisation for the unsupervised categorisation of images," IET Image Processing, vol. 14, (9) pp. 1769-1776, 2020.

Abstract

© The Institution of Engineering and Technology 2020 The bag-of-visual words (BOVWs) have been recognised as an effective mean of representing images for image classification. However, its reliance on a visual codebook developed using handcrafted image feature extraction algorithms and vector quantisation via k-means clustering often results in significant computational overhead, and poor classification accuracies. Therefore, this study presents an adaptive BOVW modelling, in which image feature extraction is achieved using deep feature learning and the amount of computation required for the development of visual codebook is minimised using a batch implementation of particle swarm optimisation. The proposed method is tested using Caltech-101 image dataset, and the results confirm the suitability of the proposed method in improving the categorisation performance while reducing the computational load.