Dissertation > Excellent graduate degree dissertation topics show

Image Recognition Based on Graph Statistical Feature

Author: WeiZheng
Tutor: TangJin
School: Anhui University
Course: Applied Computer Technology
Keywords: Histogram analysis Similarity measurement Shape Context EarthMover’s Distance Bag of words
CLC: TP391.41
Type: Master's thesis
Year: 2013
Downloads: 51
Quote: 0
Read: Download Dissertation

Abstract


With the development of advanced technology, images record more and more information of people’s life and their work. Therefore, image recognition draws more attention to researchers than ever before in pattern recognition. Image recognition methods based on the graph feature as a series of useful methods, has got significant progress due to researchers’ hard work. Focusing on the problem, that traditional methods could not describe the graphs under some non-rigid transformation adequately, three kinds of different methods are presented to recognize images more accurately after summarizing current researches and introducing related techniques.The main contributions and novelties in this thesis are follows:(1) Image recognition methods based on the graph feature are reviewed and then the current researches are introduced. These kinds of image recognition based on the graph feature can be divided into two key steps. One is the extracting and description of graph information. The other is the calculation of distance between two descriptors. From these two aspects, current methods are revealed in this thesis.(2)Edge Direction Histogram method, Earth Mover’s Distance, Shape Context and K-means algorithm are introduced in detail. These methods are theoretical foundation of the following chapters. Three new methods are presented based on the theoretical foundation.(3)The method based on the geometry statistical feature of graph is proposed for image recognition. In the method, graphs are built after calculating corner points. Then edge direction histogram and the edge distance histogram are counted to build the geometry statistical feature of graph. At last, fast and robust Earth Mover’s Distance is used to calculate similarity of graphs. The method describes graph with two different kinds of histograms so it can extend the content of graph.(4)The method based on structure context of graph is proposed for image recognition. Firstly, a sample point set is obtained by discrete sampling. Secondly, the graph structure context descriptor is presented based on the sample point set. At last, the improved Earth Mover’s Distance is used to measure the similarity between graph structure context descriptors. Different from traditional methods, the method uses2D-histograms to describe graph. Hence, it can perform better in experiments.(5) The method based on bag of words context of graph is proposed for image recognition. In this method, firstly graphs are built after corner points are calculated. Secondly, the graph entropy context of every sample point is used to form a codebook. Then the centers of K-means can be obtained to build the feature of graph. Thirdly, the graph bag of words context is calculated to describe the information of graph. Finally, Manhattan distance is used to calculate the similarity between descriptors. This method combines the bag of words and the graph entropy context, so it performs better than traditional methods.The results from retrieval and clustering experiments demonstrate that these three new methods describe graph more adequately, so they perform better than traditional methods. Images which have some structural features can be recognized correctly with these methods.

Related Dissertations

  1. Research and System Implementation of Image Retrieval Method Based on Fuzzy Clustering,TP391.41
  2. Attribute Reduction of Interval-valued Information System Based on Fuzzy Discernibility Matrix,O159
  3. Research and Implementation of the Image Search System Based on Interest Region Matching,TP391.41
  4. Research on Key Technology in License Plate Recognition,TP391.41
  5. Classification and Annotation of High Resolution Synthetic Aperture Radar Images Based on Extended Supervised Topic Model,TN957.52
  6. Image Recognition and Matching Based on Weber Local Feature and Shape Context,TP391.41
  7. Gait Recognition Based on Joint Points,TP391.41
  8. Product Image Retrieval Based on Shape,TP391.41
  9. The Technological Research of Content Based Trademark Pictures Retrieval,TP391.3
  10. Research on Shoeprints Matching Algorithm on the Scene Based on Shape Context,TP391.41
  11. Research and Implementation of image quality assessment and matching algorithm in fingerprint recognition system,TP391.41
  12. The Research on CAPTCHA Recognition Technology,TP391.41
  13. Object -based video surveillance detection algorithm research,TP274.4
  14. Maritime Objects Recognition and Tracking Based on Shape Appearance,TP391.41
  15. The Research of Image Retrieval Based on Shape Matching,TP391.41
  16. Research on the Recognizing and Matching and Retrieval Method of Hand Gestures Based on Computer Vision,TP391.3
  17. Research on Techniques for Video Shot Segmentation and Near-duplicate Clip Detection,TP391.41
  18. Research on Contour Based Shape Matching,TP391.41
  19. Research on Deformed Target Locate Algorithm Based on Extended Contour Description,TP274
  20. Recognition of Car Based on Loacal Feature,TP391.41
  21. The Segmentation of Deformable Object Image Base on Active Shape Model and Image Invariant Features,TP391.41

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