Large-scale annotated corpora have yielded impressive performance improvements in computer vision and multimedia content analysis. However, such datasets depend on an enormous amount of human labeling effort. When the labels correspond to well-known concepts, it is straightforward to train the annotators by giving a few examples with known answers. It is also straightforward to judge the quality of their labels. Neither is true when there are thousands of complex domain-specific labels. Training on all labels is infeasible and the quality of an annotator's judgements may be vastly different for some subsets of labels than for others. This paper proposes a set of data-driven algorithms to 1) train image annotators on how to disambiguate among automatically generated candidate labels, 2) evaluate the quality of annotators' label suggestions, and 3) weigh predictions. The algorithms adapt to the skills of each annotator both in the questions asked and the weights given to their answers. The underlying judgements are Bayesian, based on adaptive priors. We measure the benefits of these algorithms on a live user experiment related to image-based plant identification involving around 1000 people. The proposed methods are shown to enable huge gains in annotation accuracy. A standard user can correctly label around 2% of our data. This goes up to 80% with machine learning assisted training and assignment and up to almost 90% when doing a weighted combination of several annotators' labels.

Large scale biodiversity monitoring is essential for sustainable development (earth stewardship). With the recent advances in computer vision, we see the emergence of more and more effective identification tools allowing to set-up large-scale data collection platforms such as the popular [email protected] initiative that allow to reuse interaction data. Although it covers only a fraction of the world flora, this platform is already being used by more than 300K people who produce tens of thousands of validated plant observations each year. This explicitly shared and validated data is only the tip of the iceberg. The real potential relies on the millions of raw image queries submitted by the users of the mobile application for which there is no human validation. People make such requests to get information on a plant along a hike or something they find in their garden but not know anything about. Allowing the exploitation of such contents in a fully automatic way could scale up the world-wide collection of implicit plant observations by several orders of magnitude, which can complement the explicit monitoring efforts. In this paper, we first survey existing automated plant identification systems through a five-year synthesis of the PlantCLEF benchmark and an impact study of the [email protected] platform. We then focus on the implicit monitoring scenario and discuss related research challenges at the frontier of computer science and biodiversity studies. Finally, we discuss the results of a preliminary study focused on implicit monitoring of invasive species in mobile search logs. We show that the results are promising but that there is room for improvement before being able to automatically share implicit observations within international platforms.

In a typical citizen science/crowdsourcing environment, the contributors label items. When there are few labels, it is straightforward to train contributors and judge the quality of their labels by giving a few examples with known answers. Neither is true when there are thousands of domain-specific labels and annotators with heterogeneous skills. This demo paper presents an Active User Training framework implemented as a serious game called ThePlantGame. It is based on a set of data-driven algorithms allowing to (i) actively train annotators, and (ii) evaluate the quality of contributors' answers on new test items to optimize predictions.

This paper presents an interactive information retrieval scheme extending classical faceted search with fine-grained visual facets. Consistent visual words are extracted offline by constructing a matching graph at the region image level and splitting the graph in relevant clusters. Visual and textual facets are then indexed in the same faceted search engine, all of them being dynamically reranked according to their filtering efficiency before being presented to the end-user. To enhance their interpretability, each facet value is illustrated by a representative picture automatically selected from the matching graph in an adaptive way.

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.

The following paper deals with criterion used to measure communication protocol efficiency in Wireless Sensor Networks. As energy is a crucial characteristic of those networks, it is necessary to pay attention both to the energy consumption and to the distribution of energy consumption, when using communication protocols, so as to increase the lifetime of the whole network. Our aim is to present and discuss criterion designed to analyze communication protocol effectiveness. When designing, for example, communication protocols, it is really important to measure performances with a suitable metric according to our application, else it would be difficult to analyze and to improve a protocol. In this paper, we draw a list of existing criterion, and then introduce two new ones : Average Node Percentage and Monitored Interest Point Percentage. We also point out the relevance regarding the required application for each criterion.

Several routing protocols have been proposed for mobile wireless sensor networks. Some are based on variants of flooding algorithms leading to redundant copies of message unnecessarily. Despite various optimizations, such routing methods still remain inefficient. This paper deals with region-based routing which has been introduced to reduce the number of messages in the network. We propose an energy efficient routing algorithm, called Ellipse-routing, which is based on region-based routing. A virtual ellipse is built thanks to source and destination positions. So, only nodes within this region forward a message. For a given energy consumption model, we select a suitable ellipse factor and a transmission range, leading to a delivery rate close to 100% while minimizing energy consumption. Then, we extend the proposed scheme to take into account position errors. Performances of proposed algorithms are shown thanks to simulations.