Image Retrieval with Generative Model for Typicality
Image Retrieval with Generative Model for Typicality
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DOI：
10.4304/jnw.6.3.387-399
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发表时间：
2011-01
期刊：
影响因子：
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通讯作者：
Taro Tezuka;Akira Maeda
中科院分区：
文献类型：
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作者：
Taro Tezuka;Akira Maeda
最常见的图像检索任务之一是查找描述查询指定对象的最典型的图像。现有的图像搜索引擎不能有效地做到这一点，因为它们的搜索结果往往是属于不同语义概念的图像的混合物。因此，我们引入了典型性的概率模型。我们的模型由图像、符号特征和潜在的语义概念（方面）组成。假设概率最高的方面代表典型。通过收集大量的图像，我们可以使用EM算法来估计参数。估计的参数用于量化每个图像的典型程度。基于所提出的方法，我们实现了一个根据图像的典型性对图像进行排序的系统。用人工数据和实际数据进行的实验表明了该方法的有效性。
One of the most common image retrieval tasks is to find the most typical image that depicts the object specified by a query. Existing image search engines cannot efficiently do this since their search results are often a mixture of images belonging to various semantic concepts. We therefore introduce a probabilistic model for typicality. Our model consists of images, symbolic features, and latent semantic concepts (aspects). The aspect with highest probability is assumed to represent typicality. By collecting a large number of images, we can estimate parameters using EM algorithm. The estimated parameters are used to quantify the level of typicality for each image. Based on the proposed method, we have implemented a system, for ranking images by their typicality. Experiments using both artificial and real data showed the effectiveness of our method.