This paper introduces a new approach to semantic image retrieval using shape descriptors as dispersion and moment in conjunction with discriminative classifier model of latent-dynamic conditional random fields (LDCRFs). The target region is firstly localized via the background subtraction model. Then the features of dispersion and moments are employed to k-means clustering to extract object’s feature as second stage. After that, the learning process is carried out by LDCRFs. Finally, simple protocol and RDF (resource description framework) query language (i.e. SPARQL) on input text or image query is to retrieve semantic image based on sequential processes of query engine, matching module and ontology manager. Experimental findings show that our approach can be successful to retrieve images against the mammal’s benchmark with retrieving rate of 98.11%. Such outcomes are likely to compare very positively with those accessible in the literature from other researchers.

中文翻译：

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使用形状描述符和潜在动态条件随机场检索语义图像

本文介绍了一种新的基于形状描述符作为离散度和矩的语义图像检索方法，并结合了潜在动态条件随机场（LDCRF）的判别式分类器模型。首先通过背景扣除模型对目标区域进行定位。然后将色散和矩的特征应用于k-表示聚类以提取对象的特征作为第二阶段。之后，学习过程由LDCRF执行。最后，对输入文本或图像查询的简单协议和RDF（资源描述框架）查询语言（即SPARQL）是基于查询引擎，匹配模块和本体管理器的顺序过程来检索语义图像。实验结果表明，我们的方法可以成功地以哺乳动物基准检索图像，检索率为98.11％。这样的结果很可能与其他研究人员在文献中可获得的结果相比较。