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.

Face annotation for family photo album management

Abstract: In this paper, we propose a framework to semi-automatically annotate faces in family photo albums. The core of the framework is the features used to define face similarity and this results in the learning algorithm used to refine automatic face annotation. We have adopted similarity based search and relevance feedback ideas developed for content-based image retrieval and a set of simple yet effective color and texture based features, in addition to the traditional face recognition features, in performing candidate annotation search. The experimental evaluation of the proposed approach has been conducted with a family album of 1707 photos and the results show that the proposed approach is an effective and efficient one for semi-automatic family photo album annotation.

Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer

Abstract: Semantic annotations are vital for training models for object recognition, semantic segmentation or scene understanding. Unfortunately, pixelwise annotation of images at very large scale is labor-intensive and only little labeled data is available, particularly at instance level and for street scenes. In this paper, we propose to tackle this problem by lifting the semantic instance labeling task from 2D into 3D. Given reconstructions from stereo or laser data, we annotate static 3D scene elements with rough bounding primitives and develop a model which transfers this information into the image domain. We leverage our method to obtain 2D labels for a novel suburban video dataset which we have collected, resulting in 400k semantic and instance image annotations. A comparison of our method to state-of-the-art label transfer baselines reveals that 3D information enables more efficient annotation while at the same time resulting in improved accuracy and time-coherent labels.

A Multi-Axis Annotation Scheme for Event Temporal Relations

Abstract: Existing temporal relation (TempRel) annotation schemes often have low inter-annotator agreements (IAA) even between experts, suggesting that the current annotation task needs a better definition. This paper proposes a new multi-axis modeling to better capture the temporal structure of events. In addition, we identify that event end-points are a major source of confusion in annotation, so we also propose to annotate TempRels based on start-points only. A pilot expert annotation using the proposed scheme shows significant improvement in IAA from the conventional 60's to 80's (Cohen's Kappa). This better-defined annotation scheme further enables the use of crowdsourcing to alleviate the labor intensity for each annotator. We hope that this work can foster more interesting studies towards event understanding.

Methods of annotating a collaborative application display

Abstract: Disclosed are methods for remote users of a collaborative application to generate annotation information, send that annotation information to an application sharer device, and receive back a display combining output of the collaborative application with the annotation information. A collaborative application display is visible on an application viewer's screen. To make an annotation, a user draws over the shared display. The annotation is intercepted and sent to the sharer. On the sharer, the annotation is graphically blended with the display produced by the collaborative application. The combination is then sent to the remote viewers for display. The sharer may visually indicate, via color or a text flag, for example, the source of each annotation. The sharer may time out an annotation, or may delete the annotation if the collaborative application's display has scrolled underneath the annotation, causing the annotation to “lose its place” in the display and become meaningless.

VideoAnnEx: IBM MPEG-7 Annotation Tool for Multimedia Indexing and Concept Learning

Abstract: The VideoAnnEx, a.k.a. IBM MPEG-7 Annotation Tool, assists authors in the task of annotating video sequences with MPEG-7 metadata. Each shot in the video sequence can be annotated with static scene descriptions, key object descriptions, event descriptions, and other lexicon sets. The annotated descriptions are associated with each video shot or regions in the keyframes, and are stored as MPEG-7 XML file. The tool allows customized lexicons to be created, saved, downloaded, and updated. It can also be used to generate storyboards by saving all keyframes using JPEG. An annotationlearning component is encompassed in the tool to speed up the annotation task.