發(fā)布時間：2018-04-22 21:13

  本文選題：數(shù)字圖像處理 + 詞匯樹��； 參考：《北京郵電大學》2013年碩士論文

【摘要】：隨著現(xiàn)在智能移動終端的升級換代以及移動通信技術(shù)的高速發(fā)展,智能手機終端通過移動網(wǎng)絡接入的方式給人們帶來了全新的互聯(lián)網(wǎng)體驗。隨著圖像多媒體信息的日益增加,傳統(tǒng)的文字檢索引擎已經(jīng)無法很好的滿足用戶的檢索需求,在這樣的背景下,圖像搜索引擎開始應運而生,而這其中,以圖書檢索應用最為廣泛�；谑謾C的圖書檢索應用,一般需要根據(jù)圖書的條形碼或者圖書封面圖片作為檢索依據(jù),這樣的應用,每次只能檢索一本圖書,并且缺少對圖書相關(guān)信息的有效整合。 針對傳統(tǒng)手機圖書檢索應用的不足,綜合考慮書架圖書應用場景的特點,本文設計并實現(xiàn)了一款基于詞匯樹檢索的智能手機圖書感知系統(tǒng)。該系統(tǒng)通過手機獲取書架上排列在一起的圖書圖片上傳到服務器完成圖書檢索工作,并通過網(wǎng)頁爬蟲系統(tǒng)為手機用戶提供更為詳盡的圖書相關(guān)信息。 本系統(tǒng)為了提高檢索的準確度,首先需要區(qū)分查詢圖片中每一本相鄰圖書的書脊邊緣線。在詳細分析書架圖書的圖像特征基礎上,結(jié)合多種數(shù)字圖像處理技術(shù)的特點,通過邊緣提取、角度方向提取、過濾短邊緣、濾波、直線提取等方法提取相鄰圖書之間的邊緣線,實現(xiàn)相鄰圖書邊緣的有效分割,并通過測試驗證算法的效率以及準確性。 然后,實現(xiàn)基于詞匯樹的圖像檢索算法識別每一本圖書,該圖像檢索算法在傳統(tǒng)的SIFT特征提取算法以及視覺特征袋分類方法的基礎上,利用k-means分層聚類算法生成視覺詞匯,然后采用TF-IDF的加權(quán)方式,有效的提高圖像檢索的效率。 同時,為了整合不同網(wǎng)站的圖書信息,本文設計并實現(xiàn)了網(wǎng)頁圖書信息主題爬蟲系統(tǒng)。通過分析信息抓取的特點以及網(wǎng)站源代碼,利用該爬蟲系統(tǒng)從相應網(wǎng)站抓取需要的圖書信息并存儲到數(shù)據(jù)庫中,整合用戶較為關(guān)心的圖書信息,最終為用戶提供一款圖像檢索與Web信息檢索相結(jié)合的手機圖書感知系統(tǒng)。
[Abstract]:With the upgrading of intelligent mobile terminals and the rapid development of mobile communication technology, smart phone terminals have brought people a new Internet experience through the way of mobile network access. With the increasing of image multimedia information, the traditional text retrieval engine has been unable to meet the retrieval needs of users. In this context, the image search engine began to emerge as the times require, and among them, Book retrieval is the most widely used. The application of book retrieval based on mobile phone generally needs to be based on the bar code or the cover picture of the book as the retrieval basis. In such applications, only one book can be retrieved at a time, and there is a lack of effective integration of the relevant information of the book. Aiming at the deficiency of traditional mobile phone book retrieval application and considering the characteristics of bookshelf book application scene, this paper designs and implements a smart phone book perception system based on lexical tree retrieval. The system acquires the books arranged together on the bookshelf by mobile phone and uploads them to the server to complete the book retrieval work, and provides more detailed information about the books to the mobile phone users through the web crawler system. In order to improve the retrieval accuracy, the system first needs to distinguish the edge of each adjacent book in the query picture. Based on the detailed analysis of the image features of bookshelf books and the characteristics of various digital image processing techniques, the edge lines between adjacent books are extracted by means of edge extraction, angle direction extraction, filtering short edge, filtering, line extraction and so on. The efficient segmentation of adjacent book edges is realized, and the efficiency and accuracy of the algorithm are verified by testing. Then, the image retrieval algorithm based on lexical tree is implemented to recognize every book. Based on the traditional SIFT feature extraction algorithm and the classification method of visual feature bag, the image retrieval algorithm uses k-means hierarchical clustering algorithm to generate visual vocabulary. Then the weighted method of TF-IDF is used to improve the efficiency of image retrieval. At the same time, in order to integrate the book information of different websites, this paper designs and implements the web book information subject crawler system. By analyzing the characteristics of information capture and the source code of the website, the crawler system is used to capture the required book information from the corresponding website and store it in the database, so as to integrate the book information concerned by the user. Finally, it provides a mobile phone book perception system which combines image retrieval and Web information retrieval.
【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP391.41;TP391.3;TN929.5

【參考文獻】

相關(guān)期刊論文 前6條

1 金微;陳慧萍;;基于分層聚類的k-means算法[J];河海大學常州分校學報;2007年01期

2 何友金;李楠;;艦船紅外圖像邊緣檢測方法對比研究[J];計算機仿真;2006年04期

3 謝國強;藍立新;;基于Web的網(wǎng)絡爬蟲技術(shù)研究[J];科教文匯(中旬刊);2008年04期

4 姜毅;王兆青;曹麗;;基于HTTP的實時信息傳輸方法[J];計算機工程與設計;2008年10期

5 邢軍;基于Sobel算子數(shù)字圖像的邊緣檢測[J];微機發(fā)展;2005年09期

6 李國晶;王景強;;淺析正則表達式[J];科技資訊;2010年04期

相關(guān)碩士學位論文 前1條

1 王斐;基于增量反饋和自適應機制的主題爬蟲系統(tǒng)的設計與實現(xiàn)[D];南京理工大學;2005年

，

本文編號：1788961