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Adaptive Tree Similarity Learning for Image Retrieval

This study introduces an adaptive tree similarity learning approach for image retrieval, combining text-based and content-based methods. Results show promising performance on a dataset of 17,000 heterogeneous images. The proposed scheme offers a simple implementation with fast convergence rate.

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Adaptive Tree Similarity Learning for Image Retrieval

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  1. Adaptive tree similarity learning for image retrieval Source: ACM Multimedia Systems Journal (Springer), vol.9(2), Aug., 2003, pp.131-143 Author: Tao Wang, Yong Rui, Shi-min Hu, Jia-guang Sun Speaker: Yen-Chang Chiu Date: 2005/05/12

  2. Outline • Introduction • Related work • Proposed scheme • Experimental Result • Conclusions

  3. Introduction (1/2) • Image retrieval • Text-based image retrieval • Keyword (by human) • Content-based image retrieval (CBIR) • Features extraction (by computer): color, texture, shape, …

  4. Introduction(2/2)-CBIR

  5. Related work (1/3) • I: number of feature M: number of image in DB • i-th feature vector of m-th image • i-th feature vector of the query image q • Difference feature vector between m and q

  6. Related work (2/3) • Distance between m and q in terms of i-th feature where Wi is the low-level weights matrix of i-th feature vector • Overall distance between n and q where ui is high-level weights

  7. Related work (3/3) • Convert between distance and similarity

  8. Proposed method (1/5) • Relevance feedback techniques: • Learning mechanism Adaptive-filter based on LMS • Similarity model tree similarity model

  9. Proposed method (2/5)

  10. Proposed method (4/5)

  11. Proposed method (5/5) • π*(n) : overall similarity between query image and image n SR: pattern set of relevant image SN: pettern set of irrelevant image

  12. Experimental Result (1/4) • Test data set:『Corel』 image collection • Includes 17000 images • Heterogeneous (wide variety) • Queries: randomly generated 400 queries • Visual features: • Color moments • wavelet-based texture • water-fill edge feature

  13. Experimental Result (2/4)

  14. Experimental Result (3/4) • Precision: 檢索所得相關資料筆數 / 檢索所得之所有資料筆數 • Recall: 檢索所得之相關資料筆數 / 資料庫中所有相關資料筆數

  15. Experimental Result (4/4)

  16. Conclusions • Effective for CBIR • Simple implementation • Fast convergence rate and good performance

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