We present a supervised multi-label classification method for automatic image annotation. Our method estimates the annotation labels for a test image by accumulating similarities between the test image and labeled training images. The similarities are measured on the basis of sparse representation of the test image by the training images, which avoids similarity votes for irrelevant classes. Be...

In recent years, automatic image annotation (AIA) has been applied to cross-media retrieval usually due to its advantage of mining correlations of images and annotation texts efficiently. However, some AIA methods just annotate images as a unit and the accuracy of annotation may not be acceptable. In this paper, we propose a kind of probabilistic model which may assign keywords to an un-annotat...

Content-based image retrieval (CBIR) is currently limited because of the lack of representational power of the low-level image features, which fail to properly represent the actual contents of an image, and consequently poor results are achieved with the use of this sole information. Spatial relations represent a class of high-level image features which can improve image annotation. We apply sp...

In this paper, we describe our participation in the ImageCLEF 2015 Scalable Concept Image Annotation task. In this participation, we display our approach for an automatic image annotation by the use of an ontology-based semantic hierarchy handled at both learning and annotation steps. While recent works focused on the use of semantic hierarchies to improve concept detector accuracy, we are inve...

Automatic image annotation methods automatically assign labels to images in order to facilitate tasks such as image retrieval. Incorrect labels may negatively influence the search results so image annotation should be as accurate as possible. Labels pertaining to objects or to whole scenes are commonly used for image annotation, and precision is especially important in case when scene labels ar...

Large amounts of histology images are captured and archived in pathology departments due to the ever expanding use of digital microscopy. The ability to manage and access these collections of digital images is regarded as a key component of next generation medical imaging systems. This paper addresses the problem of retrieving histopathology images from a large collection using an example image...

In this paper we introduce the Beam Search CRM (BS-CRM) model. This model implements two novel improvements to the basic CRM [2]. First, we argue that using a Minkowski kernel allows us to capture the covariance of visual features more effectively than the standard Gaussian kernel. Second, we advocate a procedure that selects the most informative subset of tags as the image annotation. Our proc...

Nowadays, more and more images are available. However, to find a required image for an ordinary user is a challenging task. Large amount of researches on image retrieval have been carried out in the past two decades. Traditionally, research in this area focuses on content based image retrieval. However, recent research shows that there is a semantic gap between content based image retrieval and...