Describing people: A poselet-based approach to attribute classification

Bourdev, Lubomir; Maji, Subhransu; Malik, Jitendra

doi:10.1109/iccv.2011.6126413

Cited by 318 publications

(307 citation statements)

References 25 publications

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“…Most recent works have basically considered the face pattern to solve the problem [2,3,14]. Other approaches have made use of non facial features such as the whole body, the hair or clothing [4,13]. However, those approaches including non facial features, have rarely considered uncontrolled large datasets, i.e.…”

Section: Introductionmentioning

confidence: 99%

Improving Gender Classification Accuracy in the Wild

Castrillón-Santana

Lorenzo-Navarro

Ramón-Balmaseda

2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Abstract. In this paper, we focus on gender recognition in challenging large scale scenarios. Firstly, we review the literature results achieved for the problem in large datasets, and select the currently hardest dataset: The Images of Groups. Secondly, we study the extraction of features from the face and its local context to improve the recognition accuracy. Different descriptors, resolutions and classifiers are studied, overcoming previous literature results, reaching an accuracy of 89.8%.

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Section: Introductionmentioning

confidence: 99%

Improving Gender Classification Accuracy in the Wild

Castrillón-Santana

Lorenzo-Navarro

Ramón-Balmaseda

2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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“…Recent work addresses attributes of shopping images of clothing items [2,20], adding some clothing attributes to the person detection pipeline [4], and detecting clothing items and annotating attributes for an image or collection of images [6,3]. More closely related to the parsing approach used in this paper, there has been work on predicting semantic segmentations of clothing images [24,25,26], and we use the open source implementation from [26] as part of our pipeline.…”

Section: Related Workmentioning

confidence: 99%

Runway to Realway: Visual Analysis of Fashion

Vittayakorn

Yamaguchi

Berg

et al. 2015

2015 IEEE Winter Conference on Applications of Computer Vision

102

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“…Similar recent works can be found in [5] where, the authors used conditional random field (CRF) for parsing outfits. Bourdev et al [16] in their research proposed a method for recognizing attributes such as gender, hair style and types of clothes such as t-shirts, pants, jeans shorts etc. from an input image.…”

Section: Garment Product Segmentation and Type Identificationmentioning

confidence: 99%

An Automated System for Garment Texture Design Class Identification

2015

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Automatic identification of garment design class might play an important role in the garments and fashion industry. To achieve this, essential initial works are found in the literature. For example, construction of a garment database, automatic segmentation of garments from real life images, categorizing them into the type of garments such as shirts, jackets, tops, skirts, etc. It is now essential to find a system such that it will be possible to identify the particular design (printed, striped or single color) of garment product for an automated system to recommend the garment trends. In this paper, we have focused on this specific issue and thus propose two new descriptors namely Completed CENTRIST (cCENTRIST) and Ternary CENTRIST (tCENTRIST). To test these descriptors, we used two different publically available databases. The experimental results of these databases demonstrate that both cCENTRIST and tCENTRIST achieve nearly about 3% more accuracy than the existing state-of-the art methods.

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Describing people: A poselet-based approach to attribute classification

Abstract: We

Cited by 318 publications

References 25 publications

Improving Gender Classification Accuracy in the Wild

Improving Gender Classification Accuracy in the Wild

Runway to Realway: Visual Analysis of Fashion

An Automated System for Garment Texture Design Class Identification

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