This paper describes UAIC’s system built for participating in the Scalable Concept Image Annotation challenge 2015. We submitted runs both for Subtask 1 (Image Concept detection and localisation) and for Subtask 2 (Generation of Textual Descriptions of Images). For the first subtask we created an ontology with relations between concepts and their synonyms, hyponyms and hypernyms and also with r...

Automatic image annotation is crucial for keyword-based image retrieval because it can be used to improve the textual description of images. In this paper, we propose a unified framework for image annotation, which contains two kinds of learning processes and incorporates three kinds of relations among images and keywords. In addition, we propose some improvements on its components, i.e. a rein...

This paper describes the different experiments that have been conducted by the MRIM group at the LIG in Grenoble for the ImageCLEF 2009 campaign. The group participated in the following tasks: Image Retrieval and Image Annotation. For the Image Retrieval task, we submitted runs with both text and image features, and a diversification process was applied. For the Image Annotation task, we used s...

A large amount of recent research has focused on tasks that combine language and vision, resulting in a proliferation of datasets and methods. One such task is action recognition, whose applications include image annotation, scene understanding and image retrieval. In this survey, we categorize the existing approaches based on how they conceptualize this problem and provide a detailed review of...

Image classification arises as an important phase in the overall process of automatic image annotation and image retrieval. Usually, a set of manually annotated images is used to train supervised systems and classify images into classes. The act of crowdsourcing has largely focused on investigating strategies for reducing the time, cost and effort required for the creation of the annotated data...

Many latent (factorized) models have been proposed for recommendation tasks like collaborative filtering and for ranking tasks like document or image retrieval and annotation. Common to all those methods is that during inference the items are scored independently by their similarity to the query in the latent embedding space. The structure of the ranked list (i.e. considering the set of items r...

Automatic image annotation has attracted lots of research interest, and effective method for image annotation. Find effectively the correlation among labels and images is a critical task for multi-label learning. Most of the existing multi-label learning methods exploit the label correlation only in the output label space, leaving the connection between label and features of images untouched. I...

Image annotation as well as classification are both critical and challenging work in computer vision research. Due to the rapid increasing number of images and inevitable biased annotation or classification by the human curator, it is desired to have an automatic way. Recently, there are lots of methods proposed regarding image classification or image annotation. However, people usually treat t...

The purpose of this paper is to outline efforts from the 2005 CLEF crosslanguage image retrieval campaign (ImageCLEF). The aim of this CLEF track is to explore the use of both text and content–based retrieval methods for cross–language image retrieval. Four tasks were offered in the ImageCLEF track: a ad–hoc retrieval from an historic photographic collection, ad–hoc retrieval from a medical col...

Recently, motivated by the search technology, a data-driven annotation approach turns up to be effective [8, 9]. Given a query image and a labeled keyword, X. J. Wang et al [8] apply the search result cluster (SRC) algorithm into a three-layer annotation model. In [9], an improvement on [8] is made by C. Wang et al, who propose a scalable search-based approach to annotate the web personal image...