A context-aware solution to annotate people in mobile devices

Figueirêdo, Hugo Feitosa de; Fernandes, Ricardo Madeira; Menezes, Luciana Cavalcante de; Baptista, Cláudio de Souza; Lacerda, Yuri Almeida; Paiva, Anselmo Cardoso de

doi:10.1145/1821748.1821775

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Since people are likely to wear the same clothes on the same day, the features of the clothes which persons wear are used to identify the persons [4]. The people arrangements in images and the co-occurrence relations among people are also used for identification of persons [1,3,7]. Furthermore, the performance of face recognition was improved by using the social relations among people [13,15].…”

Section: Introductionmentioning

confidence: 99%

Learning people co-occurrence relations by using relevance feedback for retrieving group photos

Shimizu

Nitta

Babaguchi

2011

Proceedings of the 1st ACM International Conference on Multimedia Retrieval

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This paper proposes an image retrieval method which retrieves images of a specific person from group photos. Many query-by-example methods have focused only on the visual features of the queried person. However, since socially related people such as family and friends are often taken photos together, their co-occurrence relations can be useful information. Thus, we propose an image retrieval method which uses the visual features of not only the queried person but also those who co-occur with the queried person in the same images. Relevance feedback is used to learn who co-occur with the queried person, their faces, and how strong their co-occurrence relations are. When retrieving the images of 19 persons in total from 158 images, after five feedback iterations, the recall rate of 50% was obtained by considering the people co-occurrence relations, as against 33% when considering only the queried person. With human errors in giving relevance feedback, the recall rate still improved to 40%.

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Section: Introductionmentioning

confidence: 99%

Learning people co-occurrence relations by using relevance feedback for retrieving group photos

Shimizu

Nitta

Babaguchi

2011

Proceedings of the 1st ACM International Conference on Multimedia Retrieval

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Detection of photos from the same event captured by distinct cameras

Figueirêdo

Silva

Leite

et al. 2012

Proceedings of the 18th Brazilian Symposium on Multimedia and the Web

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Currently one of the main features of events such as birthday party, wedding, conferences, and trips is the amount of photos taken from different digital cameras. The plethora of photo devices such as smartphones and tablets have contributed for this avalanche of photos. Social networks such as Facebook, Orkut, Flickr, and Picasa store a large volume of photos. As a consequence, the retrieval of such photos demands new systems. The automatic annotation of photo context metadata such as timestamp, and geographic coordinates, represents a challenge. In this paper, we propose a method for automatic event detection based on photos taken from different cameras in a same event. The proposed method aims to improve the retrieval of photos in a same event, to make cross tagging and to detect errors in the annotations.

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A context-aware solution to annotate people in mobile devices

Cited by 2 publications

References 12 publications

Learning people co-occurrence relations by using relevance feedback for retrieving group photos

Learning people co-occurrence relations by using relevance feedback for retrieving group photos

Detection of photos from the same event captured by distinct cameras

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