Object and concept recognition for content-based image retrieval
Object and concept recognition for content-based image retrieval
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发表时间：
2005
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通讯作者：
Yi Li;L. Shapiro
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作者：
Yi Li;L. Shapiro
图像中目标类别的识别问题对于图像和视频数据库的标注和索引是非常重要的。商业CBIR系统的用户更喜欢根据关键字提出他们的查询。为了帮助自动索引过程，我们将图像表示为多种类型抽象区域的特征向量集合，这些特征向量来自各种分割过程。利用这种表示，我们开发了两种新的算法来识别户外摄影场景中的对象和概念类。半监督EM-Variant算法将每个抽象区域建模为其特征空间上的高斯分布的混合。更强大的产生式/判别式学习算法是一种两阶段方法。生成阶段对图像的描述长度进行归一化，图像可以具有任意数量的提取特征。在辨别阶段，分类器学习由该固定长度描述表示的哪些图像包含目标对象。我们已经通过试验几个不同的数据集和特征组合来测试我们的方法。我们的结果表明，与已发表的结果相比，有了显著的改进。
The problem of recognizing classes of objects in images is important for annotation and indexing of image and video databases. Users of commercial CBIR systems prefer to pose their queries in terms of key words. To help automate the indexing process, we represent images as sets of feature vectors of multiple types of abstract regions, which come from various segmentation processes. With this representation, we have developed two new algorithms to recognize classes of objects and concepts in outdoor photographic scenes. The semi-supervised EM-variant algorithm models each abstract region as a mixture of Gaussian distributions over its feature space. The more powerful generative/discriminative learning algorithm is a two-phase method. The generative phase normalizes the description length of images, which can have an arbitrary number of extracted features. In the discriminative phase, a classifier learns which images, as represented by this fixed-length description, contain the target object. We have tested our approaches by experimenting with several different data sets and combinations of features. Our results showed a significant improvement over the published results.