摘要翻译:
本文提出了一种从松散标注图像中学习潜在语义分析模型的方法,用于图像自动标注和索引。训练图像中给出的标注是松散的,原因是:(1)视觉特征与标注关键词之间存在模糊对应关系;(2)注释关键词列表不完整。第二个原因促使我们在学习主题模型之前以一种简单的方式丰富不完整的注释。特别是通过度量关键词之间的相似度,将一些想象中的关键词注入到不完整的注释中。然后,使用给定的和想象的注释来学习概率主题模型,以自动注释新图像。我们在一个典型的图像和松散注释的Corel数据集上进行了实验,并将所提出的方法与目前最先进的离散注释方法(使用一组离散斑点表示图像)进行了比较。该方法将词驱动概率潜在语义分析(PLSA-words)提高到与最好的离散标注方法相当的性能,同时保持了PLSA-words的优点,即语义范围更广。
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英文标题:
《Modeling Loosely Annotated Images with Imagined Annotations》
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作者:
Hong Tang, Nozha Boujemma, Yunhao Chen
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最新提交年份:
2008
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Information Retrieval 信息检索
分类描述:Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
涵盖索引,字典,检索,内容和分析。大致包括ACM主题课程H.3.0、H.3.1、H.3.2、H.3.3和H.3.4中的材料。
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一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要:
In this paper, we present an approach to learning latent semantic analysis models from loosely annotated images for automatic image annotation and indexing. The given annotation in training images is loose due to: (1) ambiguous correspondences between visual features and annotated keywords; (2) incomplete lists of annotated keywords. The second reason motivates us to enrich the incomplete annotation in a simple way before learning topic models. In particular, some imagined keywords are poured into the incomplete annotation through measuring similarity between keywords. Then, both given and imagined annotations are used to learning probabilistic topic models for automatically annotating new images. We conduct experiments on a typical Corel dataset of images and loose annotations, and compare the proposed method with state-of-the-art discrete annotation methods (using a set of discrete blobs to represent an image). The proposed method improves word-driven probability Latent Semantic Analysis (PLSA-words) up to a comparable performance with the best discrete annotation method, while a merit of PLSA-words is still kept, i.e., a wider semantic range.
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PDF链接:
https://arxiv.org/pdf/0805.4508