Results 211 to 220 of about 129,731 (262)
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Proceedings of the 21st annual ACM symposium on User interface software and technology, 2008
Panning and zooming interfaces for exploring very large images containing billions of pixels (gigapixel images) have recently appeared on the internet. This paper addresses issues that arise when creating and rendering auditory and textual annotations for such images.
Qing Luan +4 more
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Panning and zooming interfaces for exploring very large images containing billions of pixels (gigapixel images) have recently appeared on the internet. This paper addresses issues that arise when creating and rendering auditory and textual annotations for such images.
Qing Luan +4 more
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Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries, 2004
The performance of retrieving an image in terms of text--type of queries depends heavily on the quality of the annotated descriptive metadata that describes the content of the images. However, effective annotation of an image can often be a laborious task that requires consistent domain knowledge.
Chen-Yu Lee, Von-Wun Soo, Yi-Ting Fu
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The performance of retrieving an image in terms of text--type of queries depends heavily on the quality of the annotated descriptive metadata that describes the content of the images. However, effective annotation of an image can often be a laborious task that requires consistent domain knowledge.
Chen-Yu Lee, Von-Wun Soo, Yi-Ting Fu
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Block Annotation: Better Image Annotation With Sub-Image Decomposition
2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019Image datasets with high-quality pixel-level annotations are valuable for semantic segmentation: labelling every pixel in an image ensures that rare classes and small objects are annotated. However, full-image annotations are expensive, with experts spending up to 90 minutes per image.
Hubert Lin, Paul Upchurch, Kavita Bala
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Automatic image annotation refinement
2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2016Automatic image annotation methods automatically assign labels to images in order to facilitate tasks such as image retrieval, search, organizing and management. Incorrect labels may negatively influence the search results so image annotation should be as accurate as possible.
Miran Pobar, Marina Ivasic-Kos
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A survey on automatic image annotation
Applied Intelligence, 2020Automatic image annotation is a crucial area in computer vision, which plays a significant role in image retrieval, image description, and so on. Along with the internet technique developing, there are numerous images posted on the web, resulting in the fact that it is a challenge to annotate images only by humans.
Yilu Chen +3 more
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Objective-Guided Image Annotation
IEEE Transactions on Image Processing, 2013Automatic image annotation, which is usually formulated as a multi-label classification problem, is one of the major tools used to enhance the semantic understanding of web images. Many multimedia applications (e.g., tag-based image retrieval) can greatly benefit from image annotation.
Qi Mao 0001 +2 more
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Annotating Images by Mining Image Search Results
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008Although it has been studied for years by the computer vision and machine learning communities, image annotation is still far from practical. In this paper, we propose a novel attempt at model-free image annotation, which is a data-driven approach that annotates images by mining their search results.
Xin-Jing Wang +3 more
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A comparative image auto-annotation
IEEE International Symposium on Signal Processing and Information Technology, 2013The image annotation is an effective technology for improving the Web image retrieval. Many works have been proposed to increase the image auto-annotation performance. The annotation quality depends on many factors: segmentation, choice of visual features...etc..
Mahdia Bakalem +2 more
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2006 International Conference on Image Processing, 2006
We propose an unsupervised approach to segment color images and annotate its regions. The annotation process uses a multi-modal thesaurus that is built from a large collection of training images by learning associations between low-level visual features and keywords.
Hichem Frigui, Joshua Caudill
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We propose an unsupervised approach to segment color images and annotate its regions. The annotation process uses a multi-modal thesaurus that is built from a large collection of training images by learning associations between low-level visual features and keywords.
Hichem Frigui, Joshua Caudill
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From Image Annotation to Image Description
2012In this paper, we address the problem of automatically generating a description of an image from its annotation. Previous approaches either use computer vision techniques to first determine the labels or exploit available descriptions of the training images to either transfer or compose a new description for the test image. However, none of them report
Ankush Gupta, Prashanth Mannem
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