Paper Info
Title
Adaptive Bag-of-Visual Word Modelling using Stacked-Autoencoder and Particle Swarm Optimisation for the Unsupervised Categorisation of Images
Abstract
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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</italic> -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.
Year
DOI
Venue
2020
10.1049/iet-ipr.2019.1160
IET Image Processing
Keywords
DocType
Volume
particle swarm optimisation,vector quantisation,image classification,pattern clustering,visual databases,image representation,feature extraction,learning (artificial intelligence)
Journal
14
Issue
ISSN
Citations 
9
1751-9659
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Abass A. Olaode141.75
Golshah Naghdy2299.36