In this paper, the automatic medical annotation task of the 2007 CLEF cross language image retrieval campaign (ImageCLEF) is described. The paper focusses on the images used, the task setup, and the results obtained in the evaluation campaign. Since 2005, the medical automatic image annotation task exists in ImageCLEF with increasing complexity to evaluate the performance of state-of-the-art me...

Automatic image annotation (AIA) is expected to be a promising way to improve the performance of content-based image retrieval (CBIR). However, current image annotation results are always incomplete and noisy, and far from practical usage. In this paper, we incorporate semantic annotations into CBIR via query expansion scheme to improve retrieval accuracy. In the proposed method, semantic annot...

A novel method for automatic annotation of images is used with keywords from a generic vocabulary of concepts or objects combined with annotation-based retrieval of images. This can be done by using spatial hidden Markov model, in which states represent concepts. The parameters of this model are estimated from a set of manually annotated training images. An image in a large test collection is t...

This work focuses on fast approaches for image retrieval and classification by employing simple features to build image signatures. For this purpose a neural model for soft classification and automatic image annotation is proposed. The salient aspects of this solution are: a) the employment of a Radial Basis Function Network built on top of an image retrieval distance metric b) a soft learning ...

Although Content Based Image Retrieval (CBIR) has attracted large amount of research interest, the difficulties in querying by an example propel ultimate users towards text queries. Searching by text queries yields more effective and accurate results that meet the needs of the users while at the same time preserves their familiarity with the way traditional search engines operate. However, text...

This paper presents a new dataset of images gathered from the Web with corresponding text obtained from the webpages near where the images appeared. Already extracted features are provided to ease the dataset usage for other researchers. An initial release of 250,000 images is targeted at automatic image annotation with unsupervised data. This dataset is the one being used for the ImageCLEF 201...

Image annotation is frequently used in image base management. Unfortunately, manual keyword indexing is costly and not exempt of errors for large image bases. In this article, we present a method for automatic image description in natural language for images without problems of occlusion. This method relies on a double expertise in image indexation and natural language processing and generation.

The goal of this paper is to study the image-concept relationship as it pertains to image annotation. We demonstrate how automatic annotation of images can be implemented on partially annotated databases by learning imageconcept relationships from positive examples via inter-query learning. Latent semantic analysis (LSA), a method originally designed for text retrieval, is applied to an image/s...

Image Segmentation and Automatic Image Annotation are two research fields usually addressed independently. Treating these problems simultaneously and taking advantage of each other’s information may improve their individual results. In this work our ultimate goal is image annotation, which we perform using the hierarchical structure of irregular pyramids. We propose a new criterion to create ne...