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Temporal segmentation and keyframe selection methods for user-generated video search-based annotation. Expert Systems with Applications, 42(1), 488502.

Fernando. (2019). Finding landmarks within settled areas using hierarchical density-based clustering and meta-data from publicly available images.

Content-based image representation is a very challenging task if we restrict to their visual content. However, associated metadata (such as tags or geolocation) become a valuable source of complementary information that may help to enhance the current system performance. In this paper, we propose an automatic training framework that uses both image visual contents and metadata to fine tune deep Convolutional Neural Networks (CNNs), providing better image descriptors adapted to certain locations, such as cities or regions. Specifically, we propose to estimate some weak labels by combining visual-and location-related information and incorporate them to a novel loss-function over pairs of images. Our experiments on a landmark discovery task show that this novel training procedure enhances the performance up to a 55% over well-established CNNbased models and is free from overfitting.