Consumer image retrieval by estimating relation tree from family photo collections

Zhang, Tong; Chao, Hui; Willis, Chris; Tretter, Daniel

doi:10.1145/1816041.1816065

Cited by 12 publications

(10 citation statements)

References 10 publications

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“…Spatial relationships of faces have been used to improve age and gender estimation [8] [9], pairwise social relationship analysis [10] and photo organization [11] [12]. In these approaches, spatial relationships are represented by either pairwise relationships [8][9] [10] or nearest neighbors [8].…”

Section: Related Workmentioning

confidence: 99%

Spatial Face Context with Gender Information for Group Photo Similarity Assessment

Chiu

Hsu

Huang

2014

2014 22nd International Conference on Pattern Recognition

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Spatial arrangements of people in a photo reflect how people regard themselves in a group. Comparing spatial arrangements of subjects in two group photos can help computer understand similar social semantics and events. In this paper, we incorporate gender information with spatial arrangements of subjects to assess the visual similarity between two group photos. For each group photo, detected faces are represented by a spatial face context. Then, corresponding vertices of spatial face contexts of two group photos are retrieved by graph matching. Gender information and relative face positions are applied to the graph matching results to compute the similarity score. The experimental results show that our method can identify group photos with similar genders of subjects and spatial arrangements compared to the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Spatial Face Context with Gender Information for Group Photo Similarity Assessment

Chiu

Hsu

Huang

2014

2014 22nd International Conference on Pattern Recognition

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“…[6], [7], for example, use gender information to primarily discover and compile social relationships based on face, gender and age information. [8] similarly focuses on estimating family relations rather than using gender as a contextual cue for recognition of individuals. [9] also considers gender information, but its main aim is to inter group activity by looking at the absolute and relative arrangement of people.…”

Section: Background and Related Workmentioning

confidence: 99%

Gender-aided people recognition in photo collections

Brenner

Izquierdo

2013

2013 IEEE 15th International Workshop on Multimedia Signal Processing (MMSP)

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We show how to recognize people based on their faces in .Consumer Photo Collections while also incorporating cont�xt Ill . the form of gender information. We devise and explore a umfied framework that has a graphical model along a distance based face description method at its core. We jointly recognize people across an entire photo collection to also consider the specifics of photos that depict multiple people. Experiments on two datasets demonstrate and validate the efTectiveness of our probabilistic approach compared to traditional methods that do not consider gender information.

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“…Wang et al [25] further proposed to involve pairwise relationships as cues for learning the correspondence between facial appearances and their names. Pairwise relationships were also adopted as an index for personal photo management [31,26] and aesthetic assessment [17] when it comes to group photos. The aforementioned works have evidenced that pairwise relationships concern the arrangement of face positions in a photo; however, they only focused on a small set of pairwise relations and limited the social contexts to one or two individuals.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, pixel distance is adopted as an informative cues to measure the interpersonal relation in a photo [25,31,26]. Unfortunately, pixel distance is sensitive to environment factors like obstacles, atypical poses and culture differences [2].…”

Section: Graph Constructionmentioning

confidence: 99%

Discovering informative social subgraphs and predicting pairwise relationships from group photos

Chen

Hsu

Liao

2012

Proceedings of the 20th ACM International Conference on Multimedia

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An increasing number of users are contributing the sheer amount of group photos (e.g., for family, classmates, colleagues, etc.) on social media for the purpose of photo sharing and social communication. There arise strong needs for automatically understanding the group types (e.g., family vs. classmates) for recommendation services (e.g., recommending a family-friendly restaurant) and even predicting the pairwise relationships (e.g., mother-child) between the people in the photo for mining implicit social connections. Interestingly, we observe that the group photos are composed of atomic subgroups corresponding to certain social relationships. For this work, we propose a novel framework to (1) connect faces of different attributes and positions as a face graph and (2) discover informative subgraphs to represent social subgroups in group photos. A group photo can be further represented by a bag-of-face-subgraphs (BoFG) -the occurring frequency of social subgroups, which is informative to categorize specific group types or events. We demonstrate the effectiveness of BoFG in recognizing family photos and achieve 30.5% relative improvement over the state-of-the-art low-level features. Moreover, we propose to predict the pairwise relationships (e.g., husband-wife) in a face graph by the co-occurrence information (e.g., co-occurring with a child) in the mined subgraphs. The experiments demonstrate that the informative social subgroups significantly outperform prior work (36% relatively) which considers merely facial attributes for determining pairwise relationships. Categories and Subject Descriptors I.4 [Image Processing and Computer Vision]: Feature Measurement General Terms Algorithms, Experimentation, Human Factors KeywordsFace graph, face subgraph mining, social relationship Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. MM'12, October 29-November 2, 2012, Nara, Japan.

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Consumer image retrieval by estimating relation tree from family photo collections

Cited by 12 publications

References 10 publications

Spatial Face Context with Gender Information for Group Photo Similarity Assessment

Spatial Face Context with Gender Information for Group Photo Similarity Assessment

Gender-aided people recognition in photo collections

Discovering informative social subgraphs and predicting pairwise relationships from group photos

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