Despite a huge leap in performance of face recognition systems in recent years, some cases remain challenging for them while being trivial for humans. This is because a human brain is exploiting much more information than the face appearance to identify a person. In this work, we aim at capturing the social context of unlabeled observed faces in order to improve face retrieval. In particular, we propose a framework that substantially improves face retrieval by exploiting the faces occurring simultaneously in a query's context to infer a multi-dimensional social context descriptor. Combining this compact structural descriptor with the individual visual face features in a common feature vector considerably increases the correct face retrieval rate and allows to disambiguate a large proportion of query results of different persons that are barely distinguishable visually.

This paper presents an automatic system able to identify visual named-entities appearing in images and videos, among a list of 25,000 entities, aggregated from Wikipedia lists, and more specific websites. DigInPix is a generic application designed to identify different kinds of entities. In this first attempt, we only focus on logo identification (more generally on legal persons). The identification process mainly relies on an efficient CBIR system, searching in an indexed image database composed of 600,000 weak-labelled images crawled from Google Images. DigInPix proposes a responsive-design html5 interface [1] for testing purposes.

Amalia.js is a new extensible and versatile HTML5 multimedia player that allows you to view any type of metadata synchronized with your video or audio streams. It manages metadata that are localized both temporally and spatially. They can also be hierarchical. Several visualization plugins have already been developped, enabling amalia.js to be deployed in a huge variety of web applications. We believe it can be used in various research areas to quickly visualize analysis results and to share them with the community. Amalia.js is an open-source software under a GPL license. It is available for download at http://ina-foss.github.io/amalia.js.

This paper considers the problem of recognizing legal entities in visual contents in a similar way to named-entity recognizers for text documents. Whereas previous works were restricted to the recognition of a few tens of logotypes, we generalize the problem to the recognition of thousands of legal persons, each being modeled by a rich corporate identity automatically built from web images. We introduce a new geometrically-consistent instance-based classification method that is shown to outperform state-of-the-art techniques on several challenging datasets while being much more scalable. Further experiments performed on an automatic web crawl of 5,824 legal entities demonstrates the scalability of the approach.

Who said What, Where and How? How are images, video and stories spreading out? Who produces the information? OTMedia addresses these questions by collecting, enriching and analysing continuously more than 1500 streams of French media from TV Radio, Web, AFP, and Twitter. Two studies on media produced by end users with the OTMedia framework are presented.

State-of-the-art visual search methods allow retrieving efficiently small rigid objects in very large image datasets (e.g. logos, paintings, etc.). User's perception of the classical query-by-window paradigm is however affected by the fact that many submitted queries actually return nothing or only junk results. We demonstrate in this demo that the perception can be radically different if the objects of interest are rather suggested to the user by pre-computing relevant clusters of instances. Impressive results involving very small objects discovered in a web collection of 110K images are demonstrated through a simple interactive GUI.

This paper presents a visual-based media event detection system based on the automatic discovery of the most circulated images across the main news media (news websites, press agencies, TV news and newspapers). Its main originality is to rely on the transmedia contextual information to denoise the raw visual detections and consequently focus on the most salient transmedia events.

This paper presents a scalable method for automatically discovering frequent visual objects in large multimedia collections even if their size is very small. It first formally revisits the problem of mining or discovering such objects, and then generalizes two kinds of existing methods for probing candidate object seeds: weighted adaptive sampling and hashing-based methods. The idea is that the collision frequencies obtained with hashing-based methods can actually be converted into a prior probability density function given as input to a weighted adaptive sampling algorithm. This allows for an evaluation of any hashing scheme effectiveness in a more generalized way, and a comparison with other priors, e.g. guided by visual saliency concerns. We then introduce a new hashing strategy, working first at the visual level, and then at the geometric level. This strategy allows us to integrate weak geometric constraints into the hashing phase itself and not only neighborhood constraints as in previous works. Experiments conducted on a new dataset introduced in this paper will show that using this new hashing-based prior allows a drastic reduction of the number of tentative probes required to discover small objects instantiated several times in a large dataset.

State-of-the-art large-scale object retrieval systems usually combine efficient Bag-of-Words indexing models with a spatial verification re-ranking stage to improve query performance. In this paper we propose to directly discover spatially verified visual words as a batch process. Contrary to previous related methods based on feature sets hashing or clustering, we suggest not trading recall for efficiency by sticking on an accurate two-stage matching strategy. The problem then rather becomes a sampling issue: how to effectively and efficiently select relevant query regions while minimizing the number of tentative probes? We therefore introduce an adaptive weighted sampling scheme, starting with some prior distribution and iteratively converging to unvisited regions. Interestingly, the proposed paradigm is generalizable to any input prior distribution, including specific visual concept detectors or efficient hashing-based methods. We show in the experiments that the proposed method allows to discover highly interpretable visual words while providing excellent recall and image representativity.