semantic gap中文是什么意思

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中文翻譯
造句

語(yǔ)義間隙

semantic adj. 語(yǔ)義(學(xué))的。
a gap 填補(bǔ)空白
gap n. 1.(墻壁、籬笆等的)裂口，裂縫；豁口，缺口。【軍事】突破口。 2.(意見(jiàn)的)齟齬，分歧；隔閡，距離，差距。 3.山峽，隘口。 4.間隙；【機(jī)械工程】火花隙；【航空】(雙翼機(jī)的)翼隔。 5.(文章等中的)脫漏，中斷；(知識(shí)等的)空白，缺陷。 a gap in historical records 史料的中斷。 credibility gap 信用差距。 generation gap代溝〔不同代的人之間的思想隔閡〕。 gaps between teeth 齒縫。 the gap between imports and exports 進(jìn)出口差額。 bridge [close, fill, stop, supply] a gap 填補(bǔ)空白，彌補(bǔ)缺陷。 stand in the gap 首當(dāng)其沖，挺身阻擋。 vt. (-pp-) 使豁裂，使生罅隙。 vi. 豁開(kāi)。
compositional semantic 合成語(yǔ)義
content, semantic 語(yǔ)意內(nèi)容
external semantic 外部語(yǔ)義
fixpoint semantic 不動(dòng)點(diǎn)語(yǔ)義; 固定點(diǎn)語(yǔ)義
internal semantic 內(nèi)部語(yǔ)義
interpretive semantic 解釋語(yǔ)義
matrix semantic 語(yǔ)意矩陣
semantic actions 語(yǔ)義動(dòng)作
semantic affinity 語(yǔ)義一致
semantic ambiguity 語(yǔ)意含混; 語(yǔ)義反常
semantic analysis 語(yǔ)義分析
semantic analyzer 語(yǔ)義分析程序
semantic anomaly 語(yǔ)義異常; 語(yǔ)義異體
semantic aphasia 意義性失語(yǔ); 語(yǔ)義性失語(yǔ)癥
semantic approach 語(yǔ)義法
semantic assumption 語(yǔ)意假定
semantic attribute 語(yǔ)義屬性
semantic attributes 語(yǔ)義屬性
semantic barriers 語(yǔ)義障礙
semantic broadening 語(yǔ)義廣義化
semantic categories 語(yǔ)義類
semantic categorization 語(yǔ)義分類

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例句與用法

The uml 2.0 specification clarifies some of the critical semantic gaps in the original version
:uml2.0規(guī)范澄清了一些在老版本中的嚴(yán)重語(yǔ)義缺陷。
However, there has a big hurdle in content-based image retrieval till the present moment, i . e ., “ semantic gap ”
但是基于內(nèi)容的圖像檢索遇到了一個(gè)棘手的問(wèn)題,即“語(yǔ)義鴻溝”。
The idea of an object dbms ( odbms ) is to store the objects as such, and thus bridge the semantic gap all the way to the database
對(duì)象數(shù)據(jù)庫(kù)管理就是將對(duì)象儲(chǔ)存然后建立提取數(shù)據(jù)的路徑.這樣,我們要查找某個(gè)數(shù)據(jù)就不必間接而又緩慢地尋找了
In order to narrow the semantic gap existing in content-based image retrieval ( cbir ), a novel retrieval technology called auto-extended multi query examples ( amqe ) is proposed
摘要為了縮短基于內(nèi)容圖像檢索存在的“語(yǔ)義鴻溝”，提出了一種自動(dòng)擴(kuò)展的多示例查詢技術(shù)。
In this thesis, we research on the bridging “ semantic gap ” and extracting the semantic feature, and then we propose two effective algorithms to extract the semantic feature
本文在解決“語(yǔ)義鴻溝”,提取語(yǔ)義特征方面作了一系列比較深入地研究,并提出了兩種有效的解決模型。
In this paper, a lot research work has been done around three points : how to abstract and index low-level feature of images, how to retrieval images in semantic level, and how to fill the semantic gap by relevant feedback technique
前者研究的是根據(jù)自動(dòng)獲取的圖像低層特征，從圖像數(shù)據(jù)庫(kù)中檢索出相關(guān)圖像；而后者研究的是如何從多種渠道獲取圖像語(yǔ)義信息，并根據(jù)語(yǔ)義檢索相關(guān)圖像。
However, this technology still faces much difficulty to bridge the semantic gap between image semantic features and the lower features, resulting in the fact that the extracted content features still mainly centered upon the lower features such as color, texture and shape
然而,由于圖像語(yǔ)義特征和低層特征的“鴻溝”問(wèn)題,給圖像檢索技術(shù)帶來(lái)了很大困難,目前提取的內(nèi)容特征仍集中于顏色、紋理、形狀等低層特征。
“ semantic gap ” is the gulf between the low-level image visual feature and high-level concepts, the images can be different of semantic concept while having similar visual feature, and they can also be different of visual feature while having the same concept
“語(yǔ)義鴻溝”是指圖像的低級(jí)視覺(jué)特征和高級(jí)語(yǔ)義特征之間的差距,由計(jì)算機(jī)計(jì)算出來(lái)的低級(jí)特征的相關(guān)性很難說(shuō)明圖像在語(yǔ)義層上的相似性,語(yǔ)義層上的相似性也無(wú)法證明低級(jí)特征的相關(guān)性。
Relevance feedback techniques are important approaches closing up the semantic gap between high-level concepts and low-level features in image retrieval effectively, and efficient indexing schemes for high-dimensional data are required for real-time retrieval in large-scale image database
相關(guān)反饋方法是彌合圖像檢索中高層語(yǔ)義和低層特征之間語(yǔ)義間隔的一個(gè)重要途徑，而有效的高維索引機(jī)制則是面向大規(guī)模圖像庫(kù)的檢索能夠達(dá)到實(shí)時(shí)性要求的關(guān)鍵技術(shù)。
Considering an enormous semantic gap problem between the low-level visual features and high-level semantic information of images, and the fact that the accuracy of content-based image classification and retrieval depends greatly on the description of low-level visual features, an image semantic classification approach is proposed based on multiple-hyperplanes support vector machines ( mhsvms )
摘要由于圖像的低層可視特征與高層語(yǔ)義內(nèi)容之間存在巨大的語(yǔ)義鴻溝，而基于內(nèi)容的圖像分類和檢索準(zhǔn)確性極大依賴低層可視特征的描述，本文提出了一種基于多超平面支持向量機(jī)的圖像語(yǔ)義分類方法。

其他語(yǔ)種釋義

semantic gapとは意味：意味上のギャップ

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