Topic
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.
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07 Jul 2008
TL;DR: This paper exploits meta information of a digital photo to derive useful semantics about the digital photo and compares its results with classical relevance models used for automatic photo annotation.
Abstract: Other than the pixel information, a digital photo of today has a host of other information regarding the photo shooting event. These information are captured by different sensors present on the camera and are stored as metadata. In this paper we exploit this meta information and derive useful semantics about the digital photo. We also compare our results with classical relevance models used for automatic photo annotation. We create a dataset of digital photos containing all information and report results on it. We also make the dataset available to the community for further experiments.
41 citations
TL;DR: This paper presents a method for the automatic semantic annotation of medical images that leverages techniques from content-based image retrieval (CBIR), a well-established image search technology that uses quantifiable low-level image features to represent the high-level semantic content depicted in those images.
41 citations
28 Jun 2007
TL;DR: This paper presents the web-based annotation application Serengeti for annotating anaphoric relations which will be extended for the annotation of lexical chains.
Abstract: Annotating large text corpora is a time-consuming effort. Although single-user annotation tools are available, web-based annotation applications allow for distributed annotation and file access from different locations. In this paper we present the web-based annotation application Serengeti for annotating anaphoric relations which will be extended for the annotation of lexical chains.
41 citations
Posted Content•
TL;DR: In this paper, a deep active learning framework that combines fully convolutional network (FCN) and active learning to reduce annotation effort by making judicious suggestions on the most effective annotation areas.
Abstract: Image segmentation is a fundamental problem in biomedical image analysis. Recent advances in deep learning have achieved promising results on many biomedical image segmentation benchmarks. However, due to large variations in biomedical images (different modalities, image settings, objects, noise, etc), to utilize deep learning on a new application, it usually needs a new set of training data. This can incur a great deal of annotation effort and cost, because only biomedical experts can annotate effectively, and often there are too many instances in images (e.g., cells) to annotate. In this paper, we aim to address the following question: With limited effort (e.g., time) for annotation, what instances should be annotated in order to attain the best performance? We present a deep active learning framework that combines fully convolutional network (FCN) and active learning to significantly reduce annotation effort by making judicious suggestions on the most effective annotation areas. We utilize uncertainty and similarity information provided by FCN and formulate a generalized version of the maximum set cover problem to determine the most representative and uncertain areas for annotation. Extensive experiments using the 2015 MICCAI Gland Challenge dataset and a lymph node ultrasound image segmentation dataset show that, using annotation suggestions by our method, state-of-the-art segmentation performance can be achieved by using only 50% of training data.
41 citations