Interactive Semantic Image Retrieval = Interactive Semantic Image Retrieval

다국어 초록 (Multilingual Abstract)

The big challenge in current content-based image retrieval systems is to reduce the semantic gap between the low level-features and high-level concepts. In this paper, we have proposed a novel framework for efficient image retrieval to improve the retrieval results significantly as a means to addressing this problem. In our proposed method, we first extracted a strong set of image features by using the dual-tree rotated complex wavelet filters (DT-RCWF) and dual tree-complex wavelet transform (DT-CWT) jointly, which obtains features in 12 different directions. Second, we presented a relevance feedback (RF) framework for efficient image retrieval by employing a support vector machine (SVM), which learns the semantic relationship among images using the knowledge, based on the user interaction. Extensive experiments show that there is a significant improvement in retrieval performance with the proposed method using SVMRF compared with the retrieval performance without RF. The proposed method improves retrieval performance from 78.5% to 92.29% on the texture database in terms of retrieval accuracy and from 57.20% to 94.2% on the Corel image database, in terms of precision in a much lower number of iterations.

참고문헌 (Reference)

1 I. Selesnick, "The dual-tree complex wavelet transform" 22 (22): 123-151, 2005

2 I. J. Cox, "The Bayesian Image Retieval System, PicHunter: Theory, Implementation and Psychophysical Experiments" 9 (9): 20-37, 2000

3 M. B. Kokare, "Texture Image retrieval using New Rotated Complex Wavelet Filters" 35 (35): 1168-1178, 2005

4 S. Tong, "Support vector machine active learning with applications to text classification" 999-1006, 2000

5 S. Tong, "Support vector machine active learning for image retrieval" 107-118, 2001

6 S. Tong, "Support Vector Machines Active Learning for Image Retrieval" 2001

7 V. N. vapnik, "Statistical Learning Theory"

8 Marin Ferecatu, "Semantic interactive image retrieval combining visual and conceptual content description" 13 (13): 309-322, 2008

9 Pushpa B. Patil, "Semantic Image Retrieval using Relevance Feedback" 2 (2): 139-148, 2011

10 Anca Loredana Ion, "Semantic Based Image Retrieval using Relevance Feedback" 303-310, 2009

11 Rongtao Ding, "Research on the relevance feedback-based image retrieval in Digital library" 25 : 48-52, 2007

12 Y. Rui, "Relevance Feedback: A Power Tool in Interactive Content- Based Image Retrieval" 8 (8): 644-655, 1998

13 Zhou, X. S, "Relevance Feedback in image retrieval: A Comprehensive review" 8 (8): 536-544, 2003

14 Z. Su H, "Relevance Feedback in Content-based Image Retrieval: Bayesian framework, Feature Subspaces, and Progressive Learning,”" 12 (12): 924-936, 2003

15 Chiou-Ting Husu, "Relevance Feedback Using Generalized Bayesian Framework with Region Based optimization Learning" 14 (14): 2005

16 S. D. MacArthur, "Relevance Feedback Decision Trees in Contentbased Image Retrieva" 68-72, 2000

17 Y. Rui, "Image retrieval: Current Techniques, Promising Directions and Open Issues" 10 : 39-62, 1999

18 N. G. Kingsbury, "Image processing with complex wavelet" 357 : 2543-2560, 1999

19 R. Datta, "Image Retrieval: Ideas, Influences, and Trends of the New Age" 40 (40): 2008

20 Zhi-Hua Zhou, "Enhanced Relevance Feedback in Image Retrieval Using Unlabeled Data" 24 (24): 219-244, 2006

21 A. W. M Smeulders, "Content –based Image Retrieval at the End of the Early Years" 22 (22): 1349-1380, 2000

22 N. G. Kingsbury, "Complex wavelets for Shift Invariant Analysis and Filtering of Signals" 10 (10): 234-253, 2001

23 K. Tieu, "Boosting image retrieval" 228-235, 2003

24 C. H. Steven Hoi, "A Unified Log-Based Relevance Feedback Scheme for Image Retrieval" 18 (18): 2006

25 M. B. Kokare, "A Survey on Current Content-based Image Retrieval Methods" 48 (48): 261-271, 2002

26 Y. Rui, "-based Image Retrieval with Relevance Feedback in MARS" 1997