Dissertation > Excellent graduate degree dissertation topics show

The Research and Implemention of Image Retrieval Based on User Interested Feature

Author: LiuXiaoZhen
Tutor: ZhangTianWen
School: Harbin Institute of Technology
Course: Computer Science and Technology
Keywords: content-based image retrieval Web server log analysis user interested feature-based image retrieval
CLC: TP391.41
Type: Master's thesis
Year: 2008
Downloads: 120
Quote: 0
Read: Download Dissertation

Abstract


The combination of Web and content-based image retrieval(CBIR) is gradually becoming the future study and development trend of content-based image retrieval system。In Web-oriented system, we get users interested feature by tracking the users’accessing records and retrieve with sample image. user interested feature-based image retrieval has effectively improved the efficiency of the users’search.In this paper, we completed the following work:(1) Accessing of user interested images. By web server logs analysis and information filtering, users delete useless web logs and web log domains , get the visit records and access to users interested image, and establish a visit index table of interest image , with users’intent.(2) Color and texture feature-based image retrieval. In this paper, we extract 20-dimensional color feature with HSV space based on the cumulative color histogram and 16-dimensional texture feature with Gray co-occurrence matrix, measure similarity between two pictures respectively with histogram intersection and continental distance,normalize the results and Synthesis the total similarity.(3) User interested feature-based image retrieval. It contains the following methods: Single-user interested feature based image retrieval and Multi-user interested feature based image retrieval.Single-user interested feature based image retrieval combine sample image feature and single-user interested feature, which is obtained by compositing user interested image and forgotten factor.Multi-user interested feature based image retrieval use sample and user interested feature, which is the result of multi-user interested feature clustering.In conclusion, the two methods could improve the efficiency of retrieval through experiments.

Related Dissertations

  1. Application of Q-Learning in the Content-Based Image Retrieval Technology,TP391.41
  2. Research on Locally Features Aggregating and Indexing Algorithm in Large-scale Image Retrieval,TP391.3
  3. Content-based image retrieval technology research large-scale digital,TP391.41
  4. Image retrieval method and system for parallel computing,TP391.3
  5. Research Based on an Active Relevance Feedback Mechanism in Content-Based Image Retrieval,TP391.3
  6. Based on color and shape features of the image retrieval technology research and system design and Implementation,TP391.41
  7. Based on SVM and rough set based image retrieval relevance feedback technology research,TP391.3
  8. Content-based hierarchical commodity and emotional image retrieval research,TP391.41
  9. A Novel Method for Extracting Object-of-Interest from Natural Image by Integrating Prior Knowledge,TP391.41
  10. Content-based image retrieval key technology research,TP391.3
  11. Research and Implementation of Content-Based Image Retrieval Algorithm,TP391.3
  12. Research and Application of Content-Based Image Database Retrieval Technology,TP391.41
  13. The Research on Algorithms of Relevance Feedback and Classification in Image Retrieval,TP391.41
  14. Study of Image Retrieval with Edge and Texture Feature,TP391.3
  15. Content-Based Flower Image Retrieval,TP391.41
  16. Near Neighbor Index for SVM Retrieval,TP391.3
  17. Retrieval based on multi -level single-mode medical image retrieval system,TP391.3
  18. Research of Image Classification and Retrieval Techniques Based on SVM,TP391.41
  19. Research of Image Retrieval Technology Based on Pattern Image Production,TP391.3
  20. Image Retrieval Based on Low-level Features,TP391.3

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