single-jc.php

JACIII Vol.9 No.3 pp. 235-243
doi: 10.20965/jaciii.2005.p0235
(2005)

Paper:

Saliency-Driven Scene Learning and Recognition Based on Competitively Growing Neural Network Using Temporal Coding

Masayasu Atsumi

Department of Information Systems Science, Faculty of Engineering, Soka University, 1-236 Tangi-cho, Hachioji-shi, Tokyo 192-8577, Japan

Received:
October 30, 2004
Accepted:
December 25, 2004
Published:
May 20, 2005
Keywords:
saliency, scene memory, self-organized learning, competitive neural network, temporal coding
Abstract

This paper proposes a model of saliency-driven scene learning and recognition in which objects in saliency-driven attended spots are quickly learned and recognized based on the competitively growing neural network using temporal coding. In this model, objects in attended spots are sequentially encoded to be invariant with respect to position and size by this neural network and their positions and sizes are encoded simultaneously. This neural network represents objects using latency-based temporal coding and grows size and recognizability through self-organized learning with growth. Through simulation experiments of a robot equipped with a camera, it is shown that quick self-organized learning and glance recognition of objects in scenes are well performed by our model.

Cite this article as:
Masayasu Atsumi, “Saliency-Driven Scene Learning and Recognition Based on Competitively Growing Neural Network Using Temporal Coding,” J. Adv. Comput. Intell. Intell. Inform., Vol.9, No.3, pp. 235-243, 2005.
Data files:

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Mar. 05, 2021