Quest for relevant tags using local interaction networks and visual content

Typical tag recommendation systems for photos shared on social networks such as Flickr, use visual content analysis, collaborative filtering or personalization strategies to pro-duce annotations. However, the dependence on manual in-tervention and the knowledge of sufficient personal prefer-ences coupled with the folksonomic issues limit the scope of these strategies. In this paper, we present a fully au-tomatic 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 pref-erences. The model is learned using the collective tagging behavior of other users in the user’s local interaction net-work, which we believe approximates the user’s preferences, at least partially. The tag recommendation model gener-ates content-based annotations and then uses a Näıve Bayes formulation to translate these annotations to a set of folk-sonomic tags selected from the tags used by the users in the local interaction network. Quantitative and qualitative com-parisons 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

Generating Images Instead of Retrieving Them : Relevance Feedback on Generative Adversarial Networks

Finding images matching a user’s intention has been largely basedon matching a representation of the user’s information needs withan existing collection of images. For example, using an exampleimage or a written query to express the information need and re-trieving images that share similarities with the query or exampleimage. However, such an approach is limited to retrieving onlyimages that already exist in the underlying collection. Here, wepresent a methodology for generating images matching the userintention instead of retrieving them. The methodology utilizes arelevance feedback loop between a user and generative adversarialneural networks (GANs). GANs can generate novel photorealisticimages which are initially not present in the underlying collection,but generated in response to user feedback. We report experiments(N=29) where participants generate images using four differentdomains and various search goals with textual and image targets.The results show that the generated images match the tasks andoutperform images selected as baselines from a fixed image col-lection. Our results demonstrate that generating new informationcan be more useful for users than retrieving it from a collection ofexisting information.Peer reviewe

Learning the consensus on visual quality for nextgeneration image management

While personal and community-based image collections grow by the day, the demand for novel photo management capabilities grows with it. Recent research has shown that it is possible to learn the consensus on visual quality measures such as aesthetics with a moderate degree of success. Here, we seek to push this performance to more realistic levels and use it to (a) help select high-quality pictures from collections, and (b) eliminate low-quality ones, introducing appropriate performance metrics in each case. To achieve this, we propose a sequential arrangement of a weighted linear least squares regressor and a naive Bayes ’ classifier, applied to a set of visual features previously found useful for quality prediction. Experiments on real-world data for these tasks show promising performance, with significant improvements over a previously proposed SVM-based method

We propose IMAGINATION (IMAge Generation for

INternet AuthenticaTION), a system for the generation o

Content-based image retrieval: approaches and trends of the new age

The last decade has witnessed great interest in research on content-based image retrieval. This has paved the way for a large number of new techniques and systems, and a growing interest in associated fields to support such systems. Likewise, digital imagery has expanded its horizon in many directions, resulting in an explosion in the volume of image data required to be organized. In this paper, we discuss some of the key contributions in the current decade related to image retrieval and automated image annotation, spanning 120 references. We also discuss some of the key challenges involved in the adaptation of existing image retrieval techniques to build useful systems that can handle real-world data. We conclude with a study on the trends in volume and impact of publications in the field with respect to venues/journals and sub-topics