Annotation

About: Annotation is a research topic. Over the lifetime, 6719 publications have been published within this topic receiving 203463 citations. The topic is also known as: note & markup.

Exploiting ontologies for automatic image annotation

Abstract: Automatic image annotation is the task of automatically assigning words to an image that describe the content of the image. Machine learning approaches have been explored to model the association between words and images from an annotated set of images and generate annotations for a test image. The paper proposes methods to use a hierarchy defined on the annotation words derived from a text ontology to improve automatic image annotation and retrieval. Specifically, the hierarchy is used in the context of generating a visual vocabulary for representing images and as a framework for the proposed hierarchical classification approach for automatic image annotation. The effect of using the hierarchy in generating the visual vocabulary is demonstrated by improvements in the annotation performance of translation models. In addition to performance improvements, hierarchical classification approaches yield well to constructing multimedia ontologies.

CALBC silver standard corpus.

Abstract: The CALBC initiative aims to provide a large-scale biomedical text corpus that contains semantic annotations for named entities of different kinds. The generation of this corpus requires that the annotations from different automatic annotation systems be harmonized. In the first phase, the annotation systems from five participants (EMBL-EBI, EMC Rotterdam, NLM, JULIE Lab Jena, and Linguamatics) were gathered. All annotations were delivered in a common annotation format that included concept identifiers in the boundary assignments and that enabled comparison and alignment of the results. During the harmonization phase, the results produced from those different systems were integrated in a single harmonized corpus ("silver standard" corpus) by applying a voting scheme. We give an overview of the processed data and the principles of harmonization--formal boundary reconciliation and semantic matching of named entities. Finally, all submissions of the participants were evaluated against that silver standard corpus. We found that species and disease annotations are better standardized amongst the partners than the annotations of genes and proteins. The raw corpus is now available for additional named entity annotations. Parts of it will be made available later on for a public challenge. We expect that we can improve corpus building activities both in terms of the numbers of named entity classes being covered, as well as the size of the corpus in terms of annotated documents.

VCode and VData: illustrating a new framework for supporting the video annotation workflow

Abstract: Digital tools for annotation of video have the promise to provide immense value to researchers in disciplines ranging from psychology to ethnography to computer science. With traditional methods for annotation being cumbersome, time-consuming, and frustrating, technological solutions are situated to aid in video annotation by increasing reliability, repeatability, and workflow optimizations. Three notable limitations of existing video annotation tools are lack of support for the annotation workflow, poor representation of data on a timeline, and poor interaction techniques with video, data, and annotations. This paper details a set of design requirements intended to enhance video annotation. Our framework is grounded in existing literature, interviews with experienced coders, and ongoing discussions with researchers in multiple disciplines. Our model is demonstrated in a new system called VCode and VData. The benefit of our system is that is directly addresses the workflow and needs of both researchers and video coders.

Common annotation framework

Abstract: A Common Annotation Framework includes, in an embodiment, an annotation having a context anchor that identifies a resource and a position in the resource that the annotation pertains to, and a content anchor that identifies data that is annotating the resource. The annotation can also be extended with client application-defined data and/or functionality, and the framework can be extended with one or more of application-defined objects, methods, and annotation stores.

Web and Personal Image Annotation by Mining Label Correlation With Relaxed Visual Graph Embedding

Abstract: The number of digital images rapidly increases, and it becomes an important challenge to organize these resources effectively. As a way to facilitate image categorization and retrieval, automatic image annotation has received much research attention. Considering that there are a great number of unlabeled images available, it is beneficial to develop an effective mechanism to leverage unlabeled images for large-scale image annotation. Meanwhile, a single image is usually associated with multiple labels, which are inherently correlated to each other. A straightforward method of image annotation is to decompose the problem into multiple independent single-label problems, but this ignores the underlying correlations among different labels. In this paper, we propose a new inductive algorithm for image annotation by integrating label correlation mining and visual similarity mining into a joint framework. We first construct a graph model according to image visual features. A multilabel classifier is then trained by simultaneously uncovering the shared structure common to different labels and the visual graph embedded label prediction matrix for image annotation. We show that the globally optimal solution of the proposed framework can be obtained by performing generalized eigen-decomposition. We apply the proposed framework to both web image annotation and personal album labeling using the NUS-WIDE, MSRA MM 2.0, and Kodak image data sets, and the AUC evaluation metric. Extensive experiments on large-scale image databases collected from the web and personal album show that the proposed algorithm is capable of utilizing both labeled and unlabeled data for image annotation and outperforms other algorithms.