semantic feature 中文意思是什麼
semantic feature
解釋
語義特徵-
The syntactic, semantic feature and its pragmatic value of
語義特徵及語用價值 -
This paper tries to analyze the phenomena of semantic subjectivisation in the process of linguistic decategorization and concludes that subjectivisation is the major feature of linguistic decategorization
語言實體在非范疇化過程中，其意義也會產生一定程度的主觀化，可以說意義主觀化是非范疇化過程中的重要特徵。 -
A fuzzy image data model and a concept of fuzzy space are proposed, in which model visual feature, spatial feature and semantic feature are used for super feature in order to utilize advantage of traditional relation database as well as characteristics of image data and fuzzy retrieval. based fuzzy space, a method of similarity measurement of image is presented to support fuzzy features - based image retrieval and satisfy user ' s query requirement for image. in the thesis, a semantic template and the mechanism of dynamic relevant feedback are defined so that it can express user ' s query semantic and improve retrieval precision and useable capability for image retrieval
研究了模糊檢索方法和相關反饋機制在圖象檢索中的應用，提出了一種模糊圖象數據模型和模糊空間的概念，該模型將可視特徵、空間特徵、語義特徵看作超屬性，既充分利用了傳統關系數據庫的優點，同時又考慮了圖象數據以及模糊查詢的特點，文中提出的模糊空間和模糊相似性度量方法能支持基於模糊特徵的圖象查詢，較好地體現用戶圖象查詢的應用需求，文中定義的語義模板和相關反饋機制能在一定程度上表達用戶的查詢語義，提高圖象檢索的準確率和易用性。 -
Thorough an analysis to the semantic field of erya shitian and a further comparasion to the semantic category in traditional exegtics of erya, the categorical feature of word meaning and its limitations are disclosed, the ways people in the qin and the han dynasties understand the world and categorize things are illustrated
語義場是義位形成的系統，對《爾雅?釋天》語義場分析之後，進一步與《爾雅》傳統訓話學中語義的分類進行比較，揭示《爾雅》詞義分類的特點及其時代局限，闡釋秦漢時代人們認識事物、劃分事物類型的思維、方法及途徑。 -
In the filtering sub - system using ontology - based profile, we introduce an approach to construct the user ’ s profile based on ontology. ontology provides a formal way to describe the semantics relations between the concepts by using the means of concept - properties model. two algorithms have been designed to calculate the semantic similarity between feature vector and the profile, which have been impoved according to the evaluated results
在基於本體模板的信息過濾子系統中，本文以本體的形式來描述用戶的需求模板，利用本體中的概念關系模型來體現概念間的語義關聯關系，並設計了兩種計算文本特徵向量與本體模板語義相似度的演算法，並根據實驗結果對這兩種演算法進行了改進。 -
On the aspect of data model, based on improving and extending the gdf data model, the data model of sde was put forwad. the data model can express multi - section data and semantic relations between different feature layers. it support segment attributions, so can express a segmental attribution of a line feature
在數據模型方面，在gdf數據模型的基礎上進行了改進和擴充，提出了sde的數據模型，該模型具有多圖幅數據連續性表達、能表達不同層要素之間語義關系、支持段屬性，能夠表達線要素屬性上某一段屬性、支持多媒體數據類型等優點，並對數據庫中矢量數據的存放方式作了比較實驗。 -
In this paper, we present a new method on feature extraction which uses hownet as semantic resource, and use maximum entropy model to realize it
本文提出了一種使用知網作為語義資源選取分類特徵，並使用最大熵模型進行分類的新方法。 -
This feature perfectly combine the frequency in acoustics level and the temperament in music semantic level, we use the cosine distance of this feature to represent the similarity of two music clips, then we design a group of algorithms that is inspired from the thought of edit distance and dynamic programming. they segment the feature vectors into groups at first, then through group similarity match, group recurrent detect, merge recurrent group and structure label joined algorithms to complete the music structure label task. because this is a really new field of research and no good method of evaluation had been finding, we propose a new evaluation method and the results of the experiments show that it is a good method
然後設計了一組源於編輯距離和動態規劃思想的音樂結構分析演算法，首先將特徵向量分組，然後經過組相似匹配、組重現檢測、重現組歸並和自動標注四個前後銜接的環節實現了音樂結構的自動標注，較好地實現了將音頻形式的音樂自動標注為表示音樂結構的三元組列表形式，由於這是一個新的領域，目前還沒有比較好的量化評價方法，本文提出一種新的評價方法，並用它來評價結構分析的結果，取得了較好的效果。 -
Algorithm of feature selection for semantic video classifier
視頻語義分類特徵選擇演算法 -
“ 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
「語義鴻溝」是指圖像的低級視覺特徵和高級語義特徵之間的差距，由計算機計算出來的低級特徵的相關性很難說明圖像在語義層上的相似性，語義層上的相似性也無法證明低級特徵的相關性。 -
The feature vector, usedin the vector space model for classification, consists of variousfactors, including the semantic distance from the sentence to the topicand the distance from the sentence to the previous relevant contextoccurring before it
我們分類採用的特徵向量包含多種因素，其中包括當前句子到話題的語義距離以及當前句子到有效上文句子的距離。 -
The best result will be presented for vision feature and semantic network can cooperate properly
底層特徵和語義網路相結合取得了很好的檢索效果。 -
Secondly, because the avility of expressing image semantic informations with the color feature is too limited, so a texture feature is presented, we discuss the mothed of extracting texture of image
其次，考慮到單純顏色特徵對圖像語義信息表示能力的不足，引入了對圖像的紋理屬性進行分析，討論紋理特徵提取的各種演算法，確定提取圖像的紋理特徵矢量演算法。 -
In this paper we use the color auto - correlogram as the similarity metrics of images in low - level feature space, and change the bandwidth function. then we propose the semantic relevance feedback. the system react differently to the positive and negative user ' s feedback so that the system can go on learning after the annotation process by updating the probabilities of the list of attributes of the relevant images and reaching the real values
本文引入顏色自相關圖特徵作為圖像在底層特徵空間相鄰的度量，並修改了框架中帶寬的計算函數，然後引入反饋機制，對于用戶的正反饋和負反饋分別作不同的處理，以便在使用過程中，系統能夠繼續學習，根據反饋更新圖像的概率鏈表，使之逐漸接近真實情況。 -
Users can provide their query requirement in several different manners, the best retrieval result will be presented for the low - level feature and semantic network can cooperate properly
在檢索的過程中，用戶可以通過多種不同途徑給出查詢條件，圖像的低層特徵與語義網路互為補充以達到最佳的檢索效果。 -
The category effect on the semantic feature retrieval of natural concepts
自然概念語義特徵提取的范疇效應 -
The framework suppose that the image ' s semantic feature can be expressed by using a multi - level attribute tree, and the probability of a certain image having a certain attribute can be estimated by interpolation method using the neighboring image ' s value. here, the neighboring relation refers to the neighboring in the low - level feature space
該框架假定圖像的語義特徵可用多層屬性樹來近似表示，並且圖像擁有某個屬性的概率值可由相鄰的圖像的概率值進行插值估計，框架中的相鄰指圖像在底層特徵空間的相鄰。 -
This paper has a further study on the key technology called validity maintenance mechanism in semantic feature modeling in order to maintain the intent of designers and satisfy them. the method combines geometry model and semantic feature modeling, setting up new feature representation 、 cell element naming and identification method. in the meantime, a history - independence interactive feature boundary re - evaluation algorithm is presented based on feature editing. moreover, the validity recovery mechanism after invalid feature operation can maintain the original feature model. the above ideas have been realized on hust - caid ( computer aided industry design system developed by harbin university of science and technology ) preliminarily
為了能夠正確地維護設計者的設計意圖和滿足用戶的需求，本文在原有造型的基礎上對語義特徵造型中的關鍵技術即有效性維護機制進行深入的研究，將幾何模型，語義特徵模型結合起來統一進行研究，建立了語義特徵造型在產品模型設計過程中特徵表示方法的新理論、細胞元素命名方法和辨別機制，同時對系統原有特徵編輯過程進行了研究，提出了獨立於歷程樹的特徵邊界重構演算法以及模型操作無效時的有效性恢復方法。 -
During the retrieval process, the user ' s perception subjectivity is captured so as to realize retrieving imagf by semantic feature
實驗表明採用本文方法的檢索結果更加符合人的視覺主觀性，並能達到語義級圖像檢索。 -
As we know, retrieving image through semantic features is the most desirable and useful way. in this thesis, a semantic feature databases built dynamically based on relevance feedback was proposed
提出基於種子圖像為檢索範例圖像，採用相關性反饋的方法來動態構造語義特徵數據庫，最終實現基於語義的圖像檢索方法。
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