本文選題：圖像搜索 + 視覺認(rèn)知　； 參考：《北京科技大學(xué)》2015年博士論文

【摘要】：圖像模式識(shí)別的主要挑戰(zhàn)在于如圖像檢索和理解等不斷變化的高層次處理要求、難于表達(dá)的圖像內(nèi)容以及圖像表達(dá)的數(shù)宇陣列與通�？梢员蝗祟愃邮艿母拍罨瘍�(nèi)容之間的語(yǔ)義鴻溝。本文在視覺認(rèn)知獲得圖像視覺信息的基礎(chǔ)上,將視覺搜索與認(rèn)知階段的語(yǔ)義判定等高層處理聯(lián)系起來(lái),使圖像區(qū)域或圖像塊與圖像語(yǔ)義和圖像類別之間建立感知聯(lián)系,采用神經(jīng)索引方法把感知到的興趣內(nèi)容進(jìn)行索引和存儲(chǔ)并通過認(rèn)知粗糙集的處理實(shí)現(xiàn)了認(rèn)知規(guī)律的發(fā)現(xiàn),在任務(wù)事件的聯(lián)合驅(qū)動(dòng)下完成對(duì)圖像的協(xié)同搜索和識(shí)別。 通過動(dòng)眼對(duì)圖像區(qū)域進(jìn)行掃描和搜索,對(duì)注意力焦點(diǎn)附近的圖像片進(jìn)行視覺關(guān)聯(lián)學(xué)習(xí),建立視覺特征與語(yǔ)義特征之間的感知聯(lián)系,使圖像的視覺認(rèn)知行為和圖像識(shí)別的認(rèn)知行為連成一個(gè)整體,使圖像識(shí)別融入了視覺認(rèn)知和語(yǔ)義解釋兩個(gè)方面,符合人類的認(rèn)知規(guī)律。 圖像搜索時(shí)的特征映射與語(yǔ)義判定相結(jié)合,形成圖像語(yǔ)義類別之問的基礎(chǔ)框架聯(lián)系。在語(yǔ)義關(guān)聯(lián)的基礎(chǔ)上形成場(chǎng)景類別的語(yǔ)義表達(dá),便于用戶將感知上相似的圖像組織在一起,形成概念上下文,用戶可以解釋和標(biāo)記圖像而無(wú)需給出圖像的概念描述。 在用戶對(duì)查詢結(jié)果的反饋中,保留正相關(guān)的圖像剔除負(fù)相關(guān)圖像,動(dòng)態(tài)地實(shí)現(xiàn)了不同查詢要求下圖像數(shù)據(jù)的重組。在基礎(chǔ)框架輔助下,多層次語(yǔ)義群組關(guān)聯(lián)和多框架協(xié)同較好地復(fù)用了原有框架組織下的結(jié)果,有效地實(shí)現(xiàn)了視覺認(rèn)知、語(yǔ)義理解和查詢案例之間的聯(lián)系,滿足了圖像柔性檢索的需要。
[Abstract]:The main challenge of image pattern recognition is the changing high-level processing requirements such as image retrieval and understanding. The semantic gap between the image content which is difficult to express and the digital array of image expression and the conceptualized content that is generally accepted by human beings. On the basis of obtaining image visual information by visual cognition, this paper links visual search with high-level processing such as semantic decision in cognitive stage to establish perceptual connection between image region or image block, image semantics and image category. The perceptual content of interest is indexed and stored by neural index method, and the discovery of cognitive laws is realized by processing cognitive rough sets, and the cooperative search and recognition of images are completed under the joint driving of task events. By scanning and searching the image region through eye movement, the visual association learning of the image film near the focus of attention is carried out, and the perceptual relationship between the visual feature and the semantic feature is established. The visual cognitive behavior of the image and the cognitive behavior of the image recognition are combined into a whole, and the image recognition is integrated into the visual cognition and the semantic interpretation, which accords with the cognitive law of human beings. The feature mapping in image search is combined with semantic decision to form the basic framework of image semantic category. The semantic representation of scene categories is formed on the basis of semantic association. It is convenient for users to organize perceptually similar images together and form conceptual context. Users can interpret and mark images without giving the conceptual description of images. In the feedback of the user to the query result, the positive correlation image is retained to remove the negative correlation image, and the recombination of the image data under different query requirements is realized dynamically. With the help of the basic framework, multi-level semantic group association and multi-frame collaboration can reuse the results of the original framework, and realize the connection between visual cognition, semantic understanding and query cases. It meets the need of flexible image retrieval.
【學(xué)位授予單位】：北京科技大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2015
【分類號(hào)】：TP391.41

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本文編號(hào)：1821809