Visual attribute classification using feature selection and convolutional neural network

Rongqiang Qian, Yong Yue, Frans Coenen, Bailing Zhang

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

7 Citations (Scopus)


Visual attribute classification has been widely discussed due to its impact on lots of applications, such as face recognition, action recognition and scene representation. Recently, Convolutional Neural Networks (CNNs) have demonstrated promising performance in image recognition, object detection and many other computer vision areas. Such networks are able to automatically learn a hierarchy of discriminate features that richly describe image content. However, dimensions of features of CNNs are usually very large. In this paper, we propose a visual attribute classification system based on feature selection and CNNs. Extensive experiments have been conducted using the Berkeley Attributes of People dataset. The best overall mean average precision (mAP) is about 89.2%.

Original languageEnglish
Title of host publicationICSP 2016 - 2016 IEEE 13th International Conference on Signal Processing, Proceedings
EditorsYuan Baozong, Ruan Qiuqi, Zhao Yao, An Gaoyun
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781509013449
Publication statusPublished - 2 Jul 2016
Event13th IEEE International Conference on Signal Processing, ICSP 2016 - Chengdu, China
Duration: 6 Nov 201610 Nov 2016

Publication series

NameInternational Conference on Signal Processing Proceedings, ICSP


Conference13th IEEE International Conference on Signal Processing, ICSP 2016


  • convolutional neural networks
  • deep learning
  • feature selection
  • visual attribute classification

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