Clothing cosegmentation for recognizing people

By Andrew C. Gallagher, Tsuhan Chen. 2008

250 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

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Citations in the corpus

Neuroaesthetics in fashion: Modeling the perception of fashionability (2015, background): “Computer vision researchers have started to be interested in the subject due to the high impact of the application domain [1, 2, 3, 6, 8, 11, 18, 29].”
Faceless Person Recognition: Privacy Implications in Social Media (2016, background): “Compared to previous social media datasets, such as [28] (∼ 600 images, 32 identities), PIPA presents a leap both in size and diversity.”
Beyond frontal faces: Improving Person Recognition using multiple cues (2015, background): “Furthermore, [11] have only labeled the frontal faces.”
Person Recognition in Personal Photo Collections (2015, methodology): “Multiple other cues have been explored, for example: attributes classification [24, 25], explicit cloth modelling [15], relative camera positions [18], social context [16, 35], space-time priors [28], and photo-album priors [34].”
Cross-Domain Image Retrieval with a Dual Attribute-Aware Ranking Network (2015, methodology): “In particular, clothing recognition has been used for contextaided people identification [13], fashion style recognition [21], occupation recognition [39], and social tribe prediction [26].”
Adversarial Image Perturbation for Privacy Protection A Game Theory Perspective (2017, background): “Our case study considers the person recognition task in social media setup [5, 38, 27], as opposed to face recognition [9] (frontal faces, good lighting) or pedestrian re-identification [2, 1] (low resolution, fixed context).”
Person Recognition in Personal Photo Collections (2017, background): “Some pre-convnet work have addressed this problem in a small scale [48], [49], [50], [51], combining”
Apparel Classification with Style (2012, methodology): “To do so, we build on top of existing work [14, 13, 28] for clothing segmentation as described in Section 3.”
Segmentation using Deformable Spatial Priors with Application to Clothing (2010, unknown): “[10], the clothing/non-clothing models in the graph cut are initialised using a learnt spatial prior.”
A data-driven approach to cleaning large face datasets (2014, methodology): “One of our goals is for the QP to label faces that are likely false positives from the face detector as outliers.”
Paper Doll Parsing: Retrieving Similar Styles to Parse Clothing Items (2013, background): “This is reflected in the growing interest in clothing related recognition papers [11, 10, 25, 16, 26, 7, 2, 4], perhaps boosted by recent advances in pose estimation [27, 3].”
Getting the look: clothing recognition and segmentation for automatic product suggestions in everyday photos (2013, background): “However, many works focus on special clothing classes and applications [3] [15] [14] [5] [2] and it is only recently that generic clothing recognition has been directly tackled [16].”
Where's Waldo: Matching people in images of crowds (2011, background): “We also note that prior authors have explored color models for matching people [7, 11, 13], co-occurrence cues [6], and other contextual cues [8, 13] in other settings.”
Describing Clothing by Semantic Attributes (2012, methodology): “same experiment settings as case 2 but with features extracted within a scaled clothing mask [5] under the face.”
Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Images (2017, unknown): “Although some datasets [12] contain such information, they are either too small or not representative of images on social networks.”
Understanding and Comparing Deep Neural Networks for Age and Gender Classification (2017, background): “8% to reportedly 91% [7] on the recent and challenging Adience benchmark [8], mirroring the overall progress on other available benchmarks such as the Images of Groups data set [12], the LFW data set [18] or the Ghallagher Collection Person data set [11].”
A survey of approaches and trends in person re-identification (2014, unknown): “FisherFaces [63] based facial features and dense sampling of colors in luminance–chrominance space (LCC) alongwith horizontal and vertical edges from clothing is combined for Re-ID in [64].”
Parsing clothing in fashion photographs (2012, background): “Vision algorithms to recognize clothing have a wide variety of potential impacts, ranging from better social understanding , to improved person identification [11], surveillance [26], computer graphics [14], or content-based image retrieval [25].”
Real-time clothing recognition in surveillance videos (2011, background): “People first studied how to segment clothes as foreground objects [4] [7] [9] [3] assuming some foreground seeds [4] or regions of interests [7] provided by users, or the same clothes appearing in different backgrounds [3].”