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Effective Image Database Search via Dimensionality Reduction

Effective Image Database Search via Dimensionality Reduction. Anders Bjorholm Dahl and Henrik Aanæs IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Outline. Introduction Methods LF-clustering Experiments and Results Discussion and Conclusion.

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Effective Image Database Search via Dimensionality Reduction

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  1. Effective Image Database Search via Dimensionality Reduction Anders Bjorholm Dahl and HenrikAanæs IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

  2. Outline • Introduction • Methods • LF-clustering • Experiments and Results • Discussion and Conclusion

  3. Introduction • The bag-of-words approach • Feature extraction from the database images • Building the bag-of-words representation • Searching with a query image

  4. Introduction The Bag-of-word Model

  5. Methods Feature representation Clustering Feature assignment Image matching

  6. Feature representation • PCA is applied to reduce the dimensionality of the feature vectors • The reduction of the SIFT descriptor is from 128 to between 3 and 12 dimensions • After dimension reduction we add color to our features • the mean RGB value in a 10 × 10 pixels patch around the localization of each feature

  7. Feature representation is the PCA reduced SIFT feature is the mean RGB values is a weighing parameter ( ) normalized to unit length normalized

  8. Clustering Similar but faster than Mean-shift clustering

  9. Feature assignment [16] D. Nisterand H. Stewenius. Scalable recognition with a vocabulary tree. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 2, pages 2161–2168, June 2006. • Similarity of images are found by comparing frequency vectors of a query image to images in the database • Give each visual words a weight[16]

  10. Image matching • Frequency vectors are compared using the norm • which is found to be superior to the euclidean distance[16] • norm gives equal weight to the overlapping and non-overlapping parts • Inverted files are used for fast image retrieval

  11. Experiments and Results http://www.vis.uky.edu/~stewe/ukbench/ • Data set • first 1400 images form [16] • a series of 4 images of the same scene • Use three of the images from one scene to train the model and the last for testing • The test result is the percentage of the correct images ranked in top 3 • data set is relatively small

  12. Experiments and Results Data set：

  13. Experiments and Results • Experiments • Color added PCA SIFT • 3, 8, and 12 dimensional PCA SIFT featuresadded features are 6, 11, and 15 dimensions • compare with SIFT features reduced with PCA to 6, 11 and 15 dimensions (without color) • Clustering experiments • LF-clustering • from 8,000 to 12,000 clusters • k-means • 10 clusters in 4 levels resulting in 10,000 clusters

  14. Experiments and Results Results

  15. Experiments and Results Results

  16. Discussion and Conclusion • did not apply LF-clustering to the 128 dimensional SIFT features, because it performed very poorly • for future work the model should be tested on a larger set of data • A problem of the design of the bag-of-words model is it static nature • not designed for adding or removing images from the database

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