Dissertation > Excellent graduate degree dissertation topics show

The Research on Algorithms of Relevance Feedback and Classification in Image Retrieval

Author: LiuLin
Tutor: LiRenFa
School: Hunan University
Course: Computer Science and Technology
Keywords: Content-based image retrieval Relevance feedback Image classification Rough set
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 127
Quote: 3
Read: Download Dissertation

Abstract


With the rapid development of Internet and multimedia technology, the amount of digital image is increasing rapidly. How to retrieve necessary image from large amount image efficiently and quickly has become increasingly important. Therefore, research on image retrieval is attracting more and more attention. Content-based image retrieval was put forward in 1990s, and it performs retrieval according to the visual features of images. Content-based image retrieval improves the precision and efficiency of image retrieval, which provides it with a capacious application fields. As key techniques of image retrieval, relevance feedback and image classification have become the hotspot in content-based image retrieval fields.As an important mathematical tool for dealing with inexact, uncertainty or vague knowledge, the advantage of rough set theory is that it can obtain the essential rule of the problem from actual data without other information, which makes it has been widely applied in the field of image retrieval in recent years. Emphasizing on this character, the thesis makes deeply research on relevance feedback and image classification with rough set as theory basis and content-based image retrieval as research background.Relevance feedback has effectively solved the problem of semantic gap in image retrieval and improved the system retrieval performance greatly. By systematically summarizing and analyzing existing relevance feedback algorithms, this thesis presents a method based on rough set. This method reduces the image features with the relevance knowledge of entropy, and the compute of comparability matching, which increases the image retrieval efficiency as a result. By adjusting the feature-weighting, the method emphasizes the interesting feature to retrieve again, which enhances the retrieval precision. The results of the experiments show the effectiveness of the algorithms.Image classification is the basis of image retrieval, and effective classification method can achieve the rapid image retrieval. The thesis inducts membership function into the image classification. With membership function, we can calculate the membership value of attribute set, and attain all the decision rules on decision table and the minimal rule set of reduced condition attribute set without core-valued. Compared with conventional Bayesian method, the results of the experiments in this thesis indicate the excellent performance in image classification field.The thesis achieves a content-based image retrieval system. The system provides an experiment tool for algorithms test and further research.

Related Dissertations

  1. The Research and Implemention of Image Retrieval Based on User Interested Feature,TP391.41
  2. Application of Q-Learning in the Content-Based Image Retrieval Technology,TP391.41
  3. Research and Implementation on Content-Based Clothing Image Retrieval,TP391.41
  4. The Discovery of User Concept Region Based on Multiple Instance Learning,TP391.41
  5. Research on Transductive Support Vector Machine and Its Application in Image Retrieval,TP391.41
  6. Research on Query Expansion Technique of Retrieval System in Biomedical Field,TP391.3
  7. Fault Diagnosis Method Based on Support Vector Machine,TP18
  8. Research on Clustering Algorithm Based on Genetic Algorithm and Rough Set Theory,TP18
  9. Based on Rough Set of Urban Areas When Traffic Green Control System Research,TP18
  10. Research and System Implementation of Image Retrieval Method Based on Fuzzy Clustering,TP391.41
  11. Incremental rough set attribute reduction,TP18
  12. Research on Content-based Trademark Image Retrieval Technology,TP391.3
  13. Research for CBIR Based on Multi-intelligent Algorithms and Image Fusion,TP391.41
  14. Research on Classification of Texture Images,TP391.41
  15. Application Study of RBF Neural Network on Urban Air Quality Evaluation,X823
  16. Reduction Algorithm for Research and Application Based on Variable Precision Rough Set,TP18
  17. Application of Information Fusion Technology of Improved D-S Evidence Theory in Fault Diagnosis of Rotating Equipments of Generating Units,TM307.1
  18. Application of Rough Set and Flex in Mid-long Term Runoff Forecasting,P338
  19. Research on and Implementation of Precise Terrain Classification for Polarimetric SAR Images,TN957.52
  20. Research on Biological Visual Features Extraction in Images Combining Sparse Coding,TP391.41
  21. Based learning and interactive method of solving the intrinsic image,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