In this work, we describe the approach proposed by the RECOD team for the Placing Task, Locale-based sub-task, at MediaEval 2015. Our approach is based on the use of as much evidence as possible (textual, visual, and/or audio descriptors) to automatically assign geographical locations to images and videos.

This paper presents the RECOD team experience in the Retrieving Diverse Social Images Task at MediaEval 2015. The teams were required to develop a diversification approach for social photo retrieval. Our proposal is based on irrelevant image filtering, reranking, rank aggregation, and diversity promotion. We proposed a multimodal approach and exploited image metadata and user credibility inform...

This paper presents the RECOD approaches used in the MediaEval 2014 Violent Scenes Detection task. Our system is based on the combination of visual, audio, and text features. We also evaluate the performance of a convolutional network as a feature extractor. We combined those features using a fusion scheme. We participated in the main and the generalization tasks.

This paper presents the RECOD team experience in the Retrieving Diverse Social Images Task at MediaEval 2016. The teams were required to develop a diversification approach for social photo retrieval. Our proposal is based on re-ranking, rank aggregation, and diversity promotion, allowing employment of textual and visual information apart or fused.

We describe the approach proposed by the RECOD team for the estimation-based sub-task of Placing Task at MediaEval 2016. Our approach uses genetic programming (GP) to combine ranked lists defined in terms of textual and visual descriptors to automatically assign geographic locations to images and videos.

Otávio A. B. Penattia,∗, Fernanda B. Silva, Eduardo Valle, Valerie Gouet-Brunet, Ricardo da S. Torres RECOD Lab, Institute of Computing (IC), University of Campinas (Unicamp) – Av. Albert Einstein, 1251, Campinas, SP, 13083-852, Brazil Department of Computer Engineering and Industrial Automation (DCA), School of Electrical and Computer Engineering (FEEC), University of Campinas (Unicamp) – Av. ...

Our team has worked on melanoma classification since early 2014 [1], and has employed deep learning with transfer learning for that task since 2015 [2]. Recently, the community has started to move from traditional techniques towards deep learning, following the general trend of computer vision [3]. Deep learning poses a challenge for medical applications, due to the need of very large training ...

This work describes the approach used by the RECOD team in the MediaEval Placing Task of 2013, in which we were required to develop an automatic scheme to assign geographical locations to images. Our approach is multimodal, considering textual and visual descriptors, which are combined by a rank aggregation strategy. We estimate the location of test images based on the coordinates of top-ranked...

This paper presents the approach used by the RECOD team to address the challenges provided in the MediaEval 2015 Affective Impact of Movies Task. We designed various video classifiers, which relied on bags of visual features, and on bags of auditory features. We combined these classifiers using different approaches, ranging from majority voting to machine-learned techniques on the training data...