An elastic net-regularized HMAX model of visual processing

Alameer, A ORCID: https://orcid.org/0000-0002-7969-3609, Ghazaeil, G, Degenaar, P and Nazarpour, K 2016, An elastic net-regularized HMAX model of visual processing , in: 2nd IET International Conference on Intelligent Signal Processing 2015 (ISP).

Full text not available from this repository.

Abstract

The hierarchical MAX (HMAX) model of human visual system has been used in robotics and autonomous systems widely. However, there is still a stark gap between human and robotic vision in observing the environment and intelligently categorizing the objects. Therefore, improving models such as the HMAX is still topical. In this work, in order to enhance the performance of HMAX in an object recognition task, we augmented it using an elastic net-regularised dictionary learning approach. We used the notion of sparse coding in the S layers of the HMAX model to extract mid- and high-level, i.e. abstract, features from input images. In addition, we used spatial pyramid pooling (SPP) at the output of higher layers to create a fixed feature vectors before feeding them into a softmax classifier. In our model, the sparse coefficients calculated by the elastic net-regularised dictionary learning algorithm were used to train and test the model. With this setup, we achieved a classification accuracy of 82.6387%∓3.7183% averaged across 5-folds which is significantly better than that achieved with the original HMAX.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: IET International Conference on Intelligent Signal Processing 2015 (ISP): Proceedings
Depositing User: A Alameer
Date Deposited: 21 Jun 2022 15:05
Last Modified: 21 Jun 2022 15:05
URI: https://usir.salford.ac.uk/id/eprint/63754

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)