Dissertation > Excellent graduate degree dissertation topics show

Research of Layout Structure-based Document Image Retrieval

Author: WangDan
Tutor: WangXiChang
School: Shandong Normal University
Course: Applied Computer Technology
Keywords: text image retrieval layout structure density feature key block feature Gaussian distance function clustering
CLC: TP391.41
Type: Master's thesis
Year: 2012
Downloads: 35
Quote: 0
Read: Download Dissertation

Abstract


With the development of multimedia technology and information age, various kinds ofimage information have been grown quickly; electronic documents have been widely used in allworks of life instead of paper forms of text images. With the size of image database increasing,the demand of information inquires to people are diversified, so how to find images peopleinterested in from image data base quickly in order to reduce cost, save time and improve thedegree of automation has become interested subject in the field of retrieval. The traditionalimage retrieval technology has been no longer content with the demand of users, thus a fastimage retrieval technology has important research value and extensive application. The retrievaltechnology of the commonly used contains two methods: text-based retrieval technology andfeature-based technology which has been wide application technology so far. The text image isthe special image that mainly contains text information as well as diagrams, and it is difficult todescribe with color and texture, so the key technology of text image retrieval is how to extractfeature and calculate similarity of the feature.Layout structure analysis plays an important role in feature extraction and image retrieval,based on the structure of layout and combined global properties as well as local characteristicsthat are used as index entry, a new text image retrieval method was proposed in the paper basedon existing algorithms. After feature extraction which’s described as vector and similaritycomparison, an algorithm based on spectral clustering was used to retrieve document imageswhich users are interested in, so the size of the data comparison was greatly reduced to someextent, which was beneficial to retrieve document image in a short time and improve theefficiency of retrieval.Firstly, the preprocessing of document images which includes noise removing, binarizationand Hough transform was done. Median filter method was used to remove isolated noise points,and then skew detection and correction was done using gradient difference combined withHough transforms, the paper used Bernsen and Ostu algorithm to binary image before theprocess of skew detection. Secondly, global and local features were detected was done usingBottom-up and Up-bottom method of layout structure. Effective area was located before featuredetection, according to finding maximum blank area, column information was extracted. Inaddition, the segmentation process of text and non-text area was segmented by MaximumGradient Difference, and paragraph feature was extracted by means of connected domain merger;Key block feature was extracted in the non-texted. Thirdly, the paper used Gaussion distancefunction to compare the distance of feature vector in the vector space model.A fast retrieval algorithm of document image was proposed in the paper, the algorithm wasfirstly divided into several classes, and further divided was completed by the thought ofclustering, which could reduce comparison time when users queried images. After finding thebiggest similarity class, the query image would be compared with each image in the class above. As a result, image candidate set was got in the last step. The result shows that the time ofretrieval was greatly reduced and retrieval efficiency was greatly improved at the same time ofensuring the accuracy of retrieval.

Related Dissertations

  1. Research and Implementation of Mining Implicit User Interest,TP311.13
  2. Gao Zhong-ying academic thought and experience and use of Bufei Decoction treatment of common diseases of the respiratory system drug law,R249.2
  3. Research and Improvement on K-Means Clustering Algorithm,TP311.13
  4. The Load Research and Comprehensive Evaluation on the Agricultural Non-Point Source Pollution in Nantong,X592
  5. BF-FCM Clustering Algorithm and Its Application in the Image Segmentation,TP391.41
  6. Research on Clustering Algorithm Based on Mutation Particle Swarm Optimization,TP18
  7. Research on K-means Optimization Clustering Algorithm,TP311.13
  8. Research on Fuzzy C-Mean Clustering Algorithm Based on Particle Swarm Optimization and Shuffled Frog Leaping Algorithm,TP18
  9. Research on Clustering Algorithm Based on Genetic Algorithm and Rough Set Theory,TP18
  10. Multilayer structure based WSN routing protocol for heterogeneous clusters,TP212.9
  11. Evolutionary Clustering Algorithm and Its Application,TP311.13
  12. Moving target trajectory analysis based Intelligent Traffic Monitoring System,TP277
  13. Study of Water-Inrush Danger Forecast of Coal Seam Floor Based on Multi Factor’s Fuzzy Clustering Approach,TD745
  14. Research on Clustering and Aligning Methods for Gene Expression Time Series Data Analysis,TP311.13
  15. Research and System Implementation of Image Retrieval Method Based on Fuzzy Clustering,TP391.41
  16. Segmentation of cDNA Microarray Image Using Fuzzy C-means Algorithm Optimized by Particle Swarm,TP391.41
  17. The Research on Intrusion Detection System Based on Machine Learning,TP393.08
  18. Research on Design Methods of Multi-Dimentional Transfer Funtion in Volume Rendering,TP391.41
  19. An Algorithm on Clustering and Anomaly Detection for Multiple Data Streams,TP311.13
  20. Multi-source image fusion technology research,TP391.41
  21. Research on QM-order Slabs Matching Problem and Load Allocation Problem in Hot Strip Mill,TF089

CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
© 2012 www.DissertationTopic.Net  Mobile