This report describes our experiments in the Content Selection subtask of the Image Annotation task of ImageClef 2016[7, 13]. Our approach is based on the fact that the human visual system concentrates mostly on local features [12]. In this respect, we trained an SVM classifier with descriptors that are based on the local features of the image, such as edges and corners. For the experimentation...

This paper outlines the technical details of a prototype system for searching and browsing over a million images from the World Wide Web using their visual contents. The system relies on two modalities for accessing images — automated image annotation and NN image network browsing. The user supplies the initial query in the form of one or more keywords and is then able to locate the desired ima...

It is important to integrate contextual information in order to improve the inaccurate results of current approaches for automatic image annotation. Graph based representations allow incorporation of such information. However, their behaviour has not been studied in this context. We conduct extensive experiments to show the properties of such representations using semantic relationships as a ty...

Object localization is an image annotation task which consists of finding the location of a target object in an image. It is common to crowdsource annotation tasks and aggregate responses to estimate the true annotation. While for other kinds of annotations consensus is simple and powerful, it cannot be applied to object localization as effectively due to the task’s rich answer space and inhere...

This paper proposes a new methodology to automatically build semantic hierarchies suitable for image annotation and classification. The building of the hierarchy is based on a new measure of semantic similarity. The proposed measure incorporates several sources of information : visual, conceptual and contextual as we defined in this paper. The aim is to provide a measure that best represents im...

We propose a novel approach to annotating weakly labelled data. In contrast to many existing approaches that perform annotation by seeking clusters of self-similar exemplars (minimising intra-class variance), we perform image annotation by selecting exemplars that have never occurred before in the much larger, and strongly annotated, negative training set (maximising inter-class variance). Comp...

ImageCLEF’s Flickr Photo Annotation and Retrieval task aims to advance the state of the art in multimedia research by providing a challenging benchmark for visual concept detection, annotation and retrieval in the context of a diverse collection of Flickr photos. The benchmark consisted of two separate but closely connected subtasks, where the objective of the first subtask was to accurately de...

In this paper we report on MICC participation to the Scalable Concept Image Annotation subtask of the ImageCLEF Photo Annotation and Retrieval competition [13]. Our goal has been to investigate the applicability of data-driven methods that have obtained good results in the field of social image annotation and retrieval to web images. These methods have been applied typically to tasks such as ta...

We present an XML annotation format (MEANING Annotation Format, MAF) specifically designed to represent and integrate different levels of linguistic annotations and a tool that provides flexible access to them (MEANING Browser). We describe our experience in integrating linguistic annotations coming from different sources, and the solutions we adopted to implement efficient access to corpora an...