Different people may describe the same object in different ways, and at varied levels of granularity (“poodle”, “dog”, “pet” or “animal”?) In this paper, we propose the idea of ‘granularityaware’ groupings where semantically related concepts are grouped across different levels of granularity to capture the variation in how different people describe the same image content. The idea is demonstrat...

In order to bridge the semantic gap between the visual context of an image and semantic concepts people would use to interpret it, we propose a multi-layered image representation model considering different amounts of knowledge needed for the interpretation of the image at each layer. Interpretation results on different semantic layers of Corel images related to outdoor scenes are presented and...

This paper addresses image annotation, i.e. labelling pixels of an image with a class among a finite set of predefined classes. We propose a new method which extracts a sample of subwindows from a set of annotated images in order to train a subwindow annotation model by using the extremely randomized trees ensemble method appropriately extended to handle high-dimensional output spaces. The anno...

Automatic image annotation has attracted a lot of attention recently as a method for facilitating semantic indexing and text-based retrieval of visual content. In this paper, we propose the use of multiple Self-Organizing Maps in modeling various semantic concepts and annotating new input images automatically. The effect of the semantic gap is compensated by annotating multiple images concurren...

We propose an approach to learning the semantics of images which allows us to automatically annotate an image with keywords and to retrieve images based on text queries. We do this using a formalism that models the generation of annotated images. We assume that every image is divided into regions, each described by a continuous-valued feature vector. Given a training set of images with annotati...

The automatic annotation of images presents a particularly complex problem for machine learning researchers. In this work we experiment with semantic models and multi-class learning for the automatic annotation of query images. We represent the images using scale invariant transformation descriptors in order to account for similar objects appearing at slightly different scales and transformatio...

The present paper introduces an approach for image semantic annotation. It discusses work in progress and reports the current state of our approach. This comprises the development of the domain ontology used for annotation, the functionalities for annotating image with an underlying ontology and search features based on these annotations. We describe a method for automatic annotation of images ...

One of the proposed tasks of the ImageCLEF 2005 campaign has been an Automatic Annotation Task. The objective is to provide the classification of a given set of 1,000 previously unseen medical (radiological) images according to 57 predefined categories covering different medical pathologies. 9,000 classified training images are given which can be used in any way to train a classifier. The Autom...

Automatic image annotation (AIA) emerges in recent years, and it attempts to replace a huge amount of manual efforts for image annotation. In this study, we propose a novel framework incorporating weighting strategy with concurrent self-organizing map (CSOM) classifier based on the concept of classification. Further, we apply this model to determine a classified weight that an image belongs to ...

With the rapidly increasing interest in machine learning based solutions for automatic image annotation, the availability of reference annotations for algorithm training is one of the major bottlenecks in the field. Crowdsourcing has evolved as a valuable option for low-cost and large-scale data annotation; however, quality control remains a major issue which needs to be addressed. To our knowl...