WillowV1, Main, Corpus, bibRecord, 001087

Semantic pyramids for gender and action recognition.

Identifieur interne : 001087 ( Main/Corpus ); précédent : 001086; suivant : 001088

Semantic pyramids for gender and action recognition.

Auteurs : Fahad Shahbaz Khan ; Joost Van De Weijer ; Rao Muhammad Anwer ; Michael Felsberg ; Carlo Gatta

Source :

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society [ 1941-0042 ] ; 2014.

RBID : pubmed:24956369

English descriptors

KwdEn :
- Actigraphy (methods), Algorithms (MeSH), Artificial Intelligence (MeSH), Biometry (methods), Female (MeSH), Humans (MeSH), Image Enhancement (methods), Image Interpretation, Computer-Assisted (methods), Male (MeSH), Pattern Recognition, Automated (methods), Reproducibility of Results (MeSH), Semantics (MeSH), Sensitivity and Specificity (MeSH), Sex Determination Analysis (methods), Whole Body Imaging (methods).
MESH :
- methods : Actigraphy, Biometry, Image Enhancement, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Sex Determination Analysis, Whole Body Imaging.
- Algorithms, Artificial Intelligence, Female, Humans, Male, Reproducibility of Results, Semantics, Sensitivity and Specificity.

Abstract

Person description is a challenging problem in computer vision. We investigated two major aspects of person description: 1) gender and 2) action recognition in still images. Most state-of-the-art approaches for gender and action recognition rely on the description of a single body part, such as face or full-body. However, relying on a single body part is suboptimal due to significant variations in scale, viewpoint, and pose in real-world images. This paper proposes a semantic pyramid approach for pose normalization. Our approach is fully automatic and based on combining information from full-body, upper-body, and face regions for gender and action recognition in still images. The proposed approach does not require any annotations for upper-body and face of a person. Instead, we rely on pretrained state-of-the-art upper-body and face detectors to automatically extract semantic information of a person. Given multiple bounding boxes from each body part detector, we then propose a simple method to select the best candidate bounding box, which is used for feature extraction. Finally, the extracted features from the full-body, upper-body, and face regions are combined into a single representation for classification. To validate the proposed approach for gender recognition, experiments are performed on three large data sets namely: 1) human attribute; 2) head-shoulder; and 3) proxemics. For action recognition, we perform experiments on four data sets most used for benchmarking action recognition in still images: 1) Sports; 2) Willow; 3) PASCAL VOC 2010; and 4) Stanford-40. Our experiments clearly demonstrate that the proposed approach, despite its simplicity, outperforms state-of-the-art methods for gender and action recognition.

DOI: 10.1109/TIP.2014.2331759
PubMed: 24956369

Links to Exploration step

pubmed:24956369

Le document en format XML

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<author><name sortKey="Van De Weijer, Joost" sort="Van De Weijer, Joost" uniqKey="Van De Weijer J" first="Joost" last="Van De Weijer">Joost Van De Weijer</name>
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<author><name sortKey="Anwer, Rao Muhammad" sort="Anwer, Rao Muhammad" uniqKey="Anwer R" first="Rao Muhammad" last="Anwer">Rao Muhammad Anwer</name>
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<author><name sortKey="Felsberg, Michael" sort="Felsberg, Michael" uniqKey="Felsberg M" first="Michael" last="Felsberg">Michael Felsberg</name>
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<author><name sortKey="Gatta, Carlo" sort="Gatta, Carlo" uniqKey="Gatta C" first="Carlo" last="Gatta">Carlo Gatta</name>
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<front><div type="abstract" xml:lang="en">Person description is a challenging problem in computer vision. We investigated two major aspects of person description: 1) gender and 2) action recognition in still images. Most state-of-the-art approaches for gender and action recognition rely on the description of a single body part, such as face or full-body. However, relying on a single body part is suboptimal due to significant variations in scale, viewpoint, and pose in real-world images. This paper proposes a semantic pyramid approach for pose normalization. Our approach is fully automatic and based on combining information from full-body, upper-body, and face regions for gender and action recognition in still images. The proposed approach does not require any annotations for upper-body and face of a person. Instead, we rely on pretrained state-of-the-art upper-body and face detectors to automatically extract semantic information of a person. Given multiple bounding boxes from each body part detector, we then propose a simple method to select the best candidate bounding box, which is used for feature extraction. Finally, the extracted features from the full-body, upper-body, and face regions are combined into a single representation for classification. To validate the proposed approach for gender recognition, experiments are performed on three large data sets namely: 1) human attribute; 2) head-shoulder; and 3) proxemics. For action recognition, we perform experiments on four data sets most used for benchmarking action recognition in still images: 1) Sports; 2) Willow; 3) PASCAL VOC 2010; and 4) Stanford-40. Our experiments clearly demonstrate that the proposed approach, despite its simplicity, outperforms state-of-the-art methods for gender and action recognition. </div>
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Semantic pyramids for gender and action recognition.

Semantic pyramids for gender and action recognition.

Source :

English descriptors

Abstract

Links to Exploration step

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri

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