论文标题
弥合本地语义概念和自然场景的视觉单词袋之间的差距
Bridging the Gap between Local Semantic Concepts and Bag of Visual Words for Natural Scene Image Retrieval
论文作者
论文摘要
本文解决了基于语义的自然场景图像检索问题。一个基于内容的图像检索系统涉及数据集中的查询映像和图像，作为低级功能的集合，并根据查询图像的特征与图像数据集中图像的特征之间的相似性检索了排名的图像列表。但是，根据用户的语义解释，被称为语义差距的语义解释而言，检索到的列表中排名最高的图像可能与查询图像有所不同。为了减少语义差距，本文研究了如何使用视觉词模型和本地语义概念的分布来进行自然场景检索。本文研究了使用不同方法来表示自然场景图像中描述的语义信息的效率，以进行图像检索。已经进行了广泛的实验工作，以研究使用语义信息以及自然和城市场景图像检索的视觉单词模型的效率。
This paper addresses the problem of semantic-based image retrieval of natural scenes. A typical content-based image retrieval system deals with the query image and images in the dataset as a collection of low-level features and retrieves a ranked list of images based on the similarities between features of the query image and features of images in the image dataset. However, top ranked images in the retrieved list, which have high similarities to the query image, may be different from the query image in terms of the semantic interpretation of the user which is known as the semantic gap. In order to reduce the semantic gap, this paper investigates how natural scene retrieval can be performed using the bag of visual word model and the distribution of local semantic concepts. The paper studies the efficiency of using different approaches for representing the semantic information, depicted in natural scene images, for image retrieval. An extensive experimental work has been conducted to study the efficiency of using semantic information as well as the bag of visual words model for natural and urban scene image retrieval.
