Multimodal Sensory Representation for Object Classification via Neocortically-inspired Algorithm

M. Kirtay, Lorenzo Vannucci, Ugo Albanese, E. Falotico, C. Laschi
{"title":"Multimodal Sensory Representation for Object Classification via Neocortically-inspired Algorithm","authors":"M. Kirtay, Lorenzo Vannucci, Ugo Albanese, E. Falotico, C. Laschi","doi":"10.1109/DEVLRN.2018.8761024","DOIUrl":null,"url":null,"abstract":"This study reports our initial work on multimodal sensory representation for object classification. To form a sensory representation we used the spatial pooling phase of the Hierarchical Temporal Memory - a Neocortically-inspired algorithm. The classification task was carried out on the Washington RGB-D dataset in which the employed method provides extraction of non-hand engineered representations (or features) from different modalities which are pixel values (RGB) and depth (D) information. These representations, both early and lately fused, were used as inputs to a machine learning algorithm to perform object classification. The obtained results show that using multimodal representations significantly improve (by 5 %) the classification performance compared to a when a single modality is used. The results also indicate that the performed method is effective for multimodal learning and different sensory modalities are complementary for the object classification. Therefore, we envision that this method can be employed for object concept formation that requires multiple sensory information to execute cognitive tasks.","PeriodicalId":236346,"journal":{"name":"2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DEVLRN.2018.8761024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

This study reports our initial work on multimodal sensory representation for object classification. To form a sensory representation we used the spatial pooling phase of the Hierarchical Temporal Memory - a Neocortically-inspired algorithm. The classification task was carried out on the Washington RGB-D dataset in which the employed method provides extraction of non-hand engineered representations (or features) from different modalities which are pixel values (RGB) and depth (D) information. These representations, both early and lately fused, were used as inputs to a machine learning algorithm to perform object classification. The obtained results show that using multimodal representations significantly improve (by 5 %) the classification performance compared to a when a single modality is used. The results also indicate that the performed method is effective for multimodal learning and different sensory modalities are complementary for the object classification. Therefore, we envision that this method can be employed for object concept formation that requires multiple sensory information to execute cognitive tasks.
基于新皮层启发算法的物体分类多模态感官表征
本研究报告了我们在物体分类的多模态感官表征方面的初步工作。为了形成感觉表征,我们使用了分层时间记忆的空间池阶段-一种受新皮层启发的算法。在华盛顿RGB-D数据集上进行分类任务,所采用的方法从不同的模态(像素值(RGB)和深度(D)信息中提取非人工工程表示(或特征)。这些早期和最近融合的表示被用作机器学习算法的输入,以执行对象分类。得到的结果表明,与使用单一模态时相比,使用多模态表示显著提高了分类性能(提高了5%)。结果还表明,该方法对多模态学习是有效的,不同的感觉模态对目标分类是互补的。因此,我们设想这种方法可以用于需要多种感官信息来执行认知任务的对象概念形成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信