Local Image Feature Extraction using Stacked-Autoencoder in the Bag-of-Visual Word modelling of Images
- Resource Type
- Conference
- Authors
- Olaode, Abass; Naghdy, Golshah
- Source
- 2019 IEEE 5th International Conference on Computer and Communications (ICCC) Computer and Communications (ICCC), 2019 IEEE 5th International Conference on. :1744-1749 Dec, 2019
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Feature extraction
Visualization
Image representation
Neurons
Machine learning
Computational modeling
Image classification
component
bag-of-visual words
deep feature learning
stacked-autoencoder
unsupervised machine learning
- Language
The Bag-of-Visual Words has been recognised as an effective mean of representing images for image classification. However, its reliance on hand crafted image feature extraction algorithms often results in significant computational overhead, and poor classification accuracies. Therefore, this paper presents a Bag-of-Visual Word Modelling in which Image Feature Extraction is achieved using Deep Feature Learning via Stacked-Autoencoder. The proposed method is tested using three image collections constituted from the Caltech 101 image collection and the results confirm the ability of deep feature learning to yield optimum image categorisation performance.