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Typical tag recommendation systems for photos shared on social networks such as Flickr, use visual content analysis, collaborative filtering or personalization strategies to produce annotations. However, the dependence on manual intervention and the knowledge of sufficient personal preferences coupled with the folksonomic issues limit the scope of these strategies. In this paper, we present a fully automatic and folksonomically scalable tag recommendation model that can recommend tags for a user's photos without an explicit knowledge of the user's personal tagging preferences. The model is learned using the collective tagging behavior of other users in the user's local interaction network, which we believe approximates the user's preferences, at least partially. The tag recommendation model generates content-based annotations and then uses a Naïve Bayes formulation to translate these annotations to a set of folksonomic tags selected from the tags used by the users in the local interaction network. Quantitative and qualitative comparisons with 890 Flickr networks show that this approach is highly useful for tag recommendation in the presence of insufficient information of a user's own preferences.

Color harmonization is an artistic technique to adjust the colors of a given image in order to enhance their visual harmony. In this paper, we present a method to automatically improve the color harmony of images. Harmonization is performed using a carefully designed optimization in the hue space, while keeping the saturation and intensity components unchanged. Finally, for videos, we pose the problem as an efficient joint optimization in space and time, thus minimizing flickering or visual artifacts in the harmonized output video. We report the performance of our algorithm on a variety of test images and video sequences.

Tagged Web images provide an abundance of labeled training examples for visual concept learning. However, the performance of automatic training data selection is susceptible to highly inaccurate tags and atypical images. Consequently, manually curated training data sets are still a preferred choice for many image annotation systems. This paper introduces ARTEMIS - a scheme to enhance automatic selection of training images using an instance-weighted mixture modeling framework. An optimization algorithm is derived to learn instance-weights in addition to mixture parameter estimation, essentially adapting to the noise associated with each example. The mechanism of hypothetical local mapping is evoked so that data in diverse mathematical forms or modalities can be cohesively treated as the system maintains tractability in optimization. Finally, training examples are selected from top-ranked images of a likelihood-based image ranking. Experiments indicate that ARTEMIS exhibits higher resilience to noise than several baselines for large training data collection. The performance of ARTEMIS-trained image annotation system is comparable with usage of manually curated data sets.

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