Object recognition in clutter color images using Hierarchical Temporal Memory combined with salient-region detection

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F18%3A00324259" target="_blank" >RIV/68407700:21730/18:00324259 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0925231218304600" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0925231218304600</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.neucom.2018.04.030" target="_blank" >10.1016/j.neucom.2018.04.030</a>

Result language

angličtina

Original language name

Object recognition in clutter color images using Hierarchical Temporal Memory combined with salient-region detection

Original language description

The essential goal of this paper is to extend the functionality of the bio-inspired intelligent HTM (Hierarchical Temporal Memory) network towards two capabilities: (i) object recognition in color images, (ii) detection of multiple objects located in clutter color images. The former extension is based on the development of a novel scheme for the application of three parallel HTM networks that separately process color, texture, and shape information in color images. For the latter HTM extension, we propose a novel system in which HTM is combined with a modified model of computational visual attention. We adopt the results of Bi et al. (2010), Hu et al. (2005), and Kučerová (2011) and add new elements for the calculation of image saliency maps. The proposed algorithm enables one to automatically locate individual objects in clutter images. For computer experiments, a special image database is created to simulate ideal single object images and cluttered images with multiple objects on an inhomogeneous background. The recognition performance of HTM alone and in combination with the salient-region detection method is evaluated. We show that the attention subsystem is able to satisfactorily locate multiple objects in clutter color images with an inhomogeneous background. We also perform benchmark calculations for two selected computer vision methods used for object detection in color clutter images. Namely, the cascade detector and template matching methods are used. Our study confirms that the proposed attention system can improve the capabilities of HTM for object classification in cluttered images. The compound system of visual attention and HTM outperforms the compared methods in both criteria (recall and correct detection rate). However, as expected, the system cannot match the recognition accuracy achieved by HTM for single object images, and thus, further research is needed.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF15_003%2F0000470" target="_blank" >EF15_003/0000470: Robotics 4 Industry 4.0</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Neurocomputing

ISSN

0925-2312

e-ISSN

1872-8286

Volume of the periodical

307

Issue of the periodical within the volume

September

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

12

Pages from-to

172-183

UT code for WoS article

000436617900016

EID of the result in the Scopus database

2-s2.0-85047060879