semantic gap

The semantic gap existing in traditional content-based image retrieval makes the results, which obtained by only matching low-level visual features, difficult to meet the actual needs.

传统基于内容的图像检索中存在的语义鸿沟问题使得仅凭图像的低层视觉特征进行匹配检索难以获得较理想的效果。

I want a language that has no larger a semantic gap than Java has today.

我想要一种语言，没有像如今的Java这么大的语义鸿沟。

In order to narrow the semantic gap, relevance feedback method is introduced into image retrieval.

为缩小语义鸿沟，人们引入了相关反馈技术。

Linking the Semantic Gap in the Content-based Image Retrieval Technology

连接基于内容图像检索技术中的语义鸿沟

In content-based image retrieval, Images with high-dimensional vector that its similarity measure is more difficult, and the semantic gap is a problem also.

而在基于内容的图像检索中，图像用高维矢量表示，其相似性度量较为困难，同时，也面临着语义鸿沟问题。

This is quite important to solve the "semantic gap" problem in image annotation.

这些对于解决图像标注中的语义鸿沟问题是十分重要的。

In the traditional approach of content-based image retrieval, the wide image domain results in the wide semantic gap between the low-level features and the high-level concepts.

在传统的基于内容图像检索的方法中，由于图像的领域较宽，图像的低级视觉特征和高级概念之间存在较大的语义间隔，检索的效果不很理想。

The semantic gap between low feature and high semantic feature makes CBVR not applicable for general users.

当中存在的低层特征和高层语义之间的语义鸿沟问题，使得CBVR还难以适用于普通用户。

In content-based image retrieval systems, the inconsistency between image low-level features and the concept of high-level expressed by images lead to system semantic gap problem.

在基于内容的图像检索系统中，图像低层特征和图像所表达高层概念之间的不一致性导致系统出现语义鸿沟问题。

How to solve the "semantic gap" is the key problem of semantic-based image retrieval.

如何解决语义鸿沟是语义图像检索的关键问题。

Due to the semantic gap between low-level feature and high-level semantic feature of an image, people are often not satisfied with the results of traditional color-based image retrieval.

由于图像的底层特征与图像的高层语义特征有较大差异，传统的基于颜色的图像检索效果常常不能尽如人意。

The problem is the semantic gap between vision features and semantic information.

但是目前为止还有许多未解决的问题，最大的就是底层特征和高层语义间的鸿沟。

In order to bridge the semantic gap, some researchers have proposed automatic image annotation.

为了弥补这个语义鸿沟，现在一些研究者提出了自动图像标注的方法。

However, there is a "semantic gap" problem in content-based image retrieval so that the retrieval accuracy cannot be infinitely increased.

但基于内容的图像检索存在语义鸿沟问题，这也是检索准确率不可能无限提高的根本原因。

The Semantic Gap which exists between video low-level features and high-level semantics is the bottleneck of the video analysis and retrieval.

视频低层特征和高层语义之间存在的“语义鸿沟”是视频分析与检索应用研究的一个技术瓶颈问题。

A novel automatic image annotation approach is proposed to bridge the semantic gap of content-based image retrieval.

针对图像检索中的语义鸿沟问题，提出了一种新颖的自动图像标注方法。

It has good human-computer interaction and can minify the "Semantic Gap". At last, an image retrieval system based on ROI is implemented in this paper.

使得系统具有较好的人机交互性能，能显著缩小语义鸿沟。最后，本文设计完成了基于ROI的网格商空间多粒度图像检索系统。

The semantic gap between image semantic and visual features will be solved in image annotation.

图像语义的标注需要解决图像高层语义和底层特征间存在的语义鸿沟。

But the "Semantic Gap" Between the Low-level Features of An Image and High-level Semantic Feature Has Became the Major Obstacle to Cbir Technology.

基于内容的图像检索虽然克服了基于文本检索方法的主观片面和工作量大的缺陷，但是低层视觉特征和高层语义之间的语义鸿沟却成为了基于内容图像检索技术前进的一大障碍。

Problems of "semantic gap" exist for classification algorithms based on low-level features such as color, texture, shape and so on.

传统的图像分类基于图像颜色、纹理和形状等底层特征，存在语义鸿沟的问题。