There are errors in Figs 9–12. In Figs 9 and 10, the images and captions have been switched. In Figs 11 and 12, the images only have been switched. Please view the correct Figs 9–12 and their captions here. The publisher apologizes for the errors. open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reprod...

Direct manipulation is the key concept of interaction with systems that use graphics to communicate with the user. The latest generation of such systems supports also interaction with other modalities, i.e., text and figure captions. However, the effects of interaction within one modality must be reflected in the other modalities in order to keep the presentation consistent. Although some appro...

Video caption detection, and evtraction is an important step for information retrieval in video databases. In this paper, we extract test information in video by fully utilizing the temporal information contained in the video. First we'create a binary abstract sequence from a video segment. By analyzing the statistical pixel changes in the sequence, we can effectively locate the (dis)appearing ...

We present a video caption detection and recognition system based on a fuzzy-clustering neural network (FCNN) classifier. Using a novel caption-transition detection scheme we locate both spatial and temporal positions of video captions with high precision and efficiency. Then employing several new character segmentation and binarization techniques, we improve the Chinese video-caption recogniti...

The captions for Figs 2 and 3 are incorrectly switched. The caption for Fig 3 should be the caption for Fig 2, and the caption for Fig 3 should be the caption for Fig 2. Please see the corrected captions here. Copyright: © 2015 Flesch et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and r...

Given a set of images with related captions, our goal is to show how visual features can improve the accuracy of unsupervised word sense disambiguation when the textual context is very small, as this sort of data is common in news and social media. We extend previous work in unsupervised text-only disambiguation with methods that integrate text and images. We construct a corpus by using Amazon ...

IN our article that appeared in November, 2013 [1], a production error occurred which resulted in the misalignment of Fig. 13, Fig. 14, Fig. 15, Fig. 16, Fig. 17, and Fig. 18 with their captions, starting from Fig. 13 to Fig. 18. As a result, a correct Fig. 13 is missing, and Fig. 18 repeats Fig. 19. We regret that this has happened. The correct figures with their corresponding captions are sho...

1.1.1 Video Captioning Datasets YouTube2Text or MSVD The Microsoft Research Video Description Corpus (MSVD) or YouTube2Text (Chen and Dolan, 2011) is used for our primary video captioning experiments. It has 1970 YouTube videos in the wild with many diverse captions in multiple languages for each video. Caption annotations to these videos are collected using Amazon Mechanical Turk (AMT). All ou...

Captions in videos contain valuable information for video retrieval. Although texts in captions can be obtained easily in the new image compression formats like MPEG2, there still are many video programs encoded in older formats. Thus, video OCR is indispensable for content-based video retrieval. This paper proposes a simple video OCR method for Chinese captions, including image capturing, capt...

Given a corpus of news items consisting of images accompanied by text captions, we want to find out “who’s doing what”, i.e. associate names and action verbs in the captions to the face and body pose of the persons in the images. We present a joint model for simultaneously solving the image-caption correspondences and learning visual appearance models for the face and pose classes occurring in ...