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Unsupervised Face Annotation Using Web-Mined Data: A Novel Framework for Visual Content Retrieval

This paper presents an innovative framework for unsupervised face annotation by mining data from the web. It addresses the challenge of efficient face recognition in large image and video databases, where traditional supervised and unsupervised learning methods are hindered by the lack of labeled data. The proposed approach includes processes for face detection, normalization, and ranking using local density scores and bagging of SVM classifiers. Results show significant improvements in retrieval performance, paving the way for enhanced tools for indexing and retrieving visual data.

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Unsupervised Face Annotation Using Web-Mined Data: A Novel Framework for Visual Content Retrieval

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  1. Unsupervised Face Annotation by Mining the Web Data Mining, 2008. ICDM '08. Eighth IEEE International Conference on Duy-Dinh Le National Institute of Informatics 2-1-2 Hitotsubashi, Chiyoda-ku Tokyo, JAPAN 101-8430 ledduy@nii.ac.jp Shin’ichi Satoh National Institute of Informatics 2-1-2 Hitotsubashi, Chiyoda-ku Tokyo, JAPAN 101-8430 satoh@nii.ac.jp Student: Tu , Chien-Hsun 69821059 LIU, Yuan-Ming 69821039

  2. Outline • Introduction • Proposed Framework -Face Processing -Ranking by Local Density Score -Ranking by Bagging of SVM Classifier • Experimental Results • Conclusion

  3. Introduction • Large image and video databases have become more available than ever to users. • This trend has shown the need for effective and efficient tools for indexing and retrieving based on visual content.

  4. Introduction • Improve the retrieval performance is to take into account visual information present in the retrieved faces. • Challenge - Facial appearance due to pose changes, illumination ,facial expressions make face recognition difficult. - No labels makes supervised and unsupervised learning methods inapplicable.

  5. System Framework

  6. Proposed Framework- Face Processing • We perform a ranking process and learning of person X’s model as follows: • Step 1: Detect faces and eye positions, and then perform face normalizations.

  7. Proposed Framework • Step 2: Compute an eigenface space and project the input faces into this subspace. • Step 3: Estimate the ranked list of these faces using Rank-By-Local-Density-Score. • Step 4: Improve this ranked list using Rank-By-Bagging-ProbSVM.

  8. Ranking by Local Density Score • Among the faces retrieved by text-based search engines for a query of person-X, relevant faces usually look similar and form the largest cluster.

  9. Ranking by Local Density Score • One approach of re-ranking these faces is to cluster based on visual similarity. • Problem Ideal clustering results is impossible since these faces are high dimensional data and the clusters are in different shapes, sizes, and densities.

  10. Ranking by Local Density Score • We use the idea of density-based clustering described by to solve this problem. • We define the local density score (LDS) of a point p (i.e. a face) as the average distance to its k-nearest neighbors.

  11. Ranking by Local Density Score • We do not directly use the Euclidean distance between two points in this feature space for distance(p, q).

  12. Ranking by Local Density Score • A high value of LDS(p, k) indicates a strong association between p and its neighbors. Therefore, we can use this local density score to rank faces.

  13. Ranking by Bagging of SVM Classifiers • Problem • One limitation of the local density score based ranking is it cannot handle faces of another person strongly associated in the k-neighbor set (for example, many duplicates). • The main idea is to use a probabilistic model to measure the relevancy of a face to person-X, P(person−X|face).

  14. Ranking by Bagging of SVM Classifiers • Improving an input rank list by combining weak classifiers trained from subsets annotated by that rank list. We set p=20% : the maximum Kendall tau distance. (set=0.05)

  15. Ranking by Bagging of SVM Classifiers • The iterations significantly improve the final ranked list.

  16. Experimental Results

  17. Experimental Results • We performed a comparison between our proposed method with other existing approaches. • Text Based Baseline (TBL) • Distance-Based Outlier (DBO) • Densest Sub-Graph based Method (DSG) • Local Density Score (LDS) • Unsupervised Ensemble Learning Using Local Density Score (UEL-LDS) • Supervised Learning (SVM-SUP)

  18. Experimental Results Performance comparison of methods.

  19. Experimental Results Distribution of retrieved faces and relevant faces of 16 individuals used in experiments.

  20. Experimental Results-Evaluation Criteria • Nret be the total number of faces returned, • Nrel the number of relevant faces • Nhit the total number of relevant faces

  21. Experimental Results

  22. Conclusion • Our approach works fairly well for well known people, where the main assumption that text-based search engines return a large fraction of relevant images is satisfied. • The aim of our future work is to study how to improve the quality of the training sets used in this iteration (bagging SVM classifiers).

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