Learning similarity measure for natural image retrieval with relevance feedback Reporter: Francis 2005/3/3

2 Outline 1. Introduction 2. Motivation of our approach 3. Constrained similarity measure 3-1 Learning with SVM (support vector machine) 3-2 Learning with AdaBoost 4. Experimental results 5. Discussion and future research

3 1. Introduction Two approaches to visual information retrieval: Using keywords or CBIR The process is very subjective for human beings. Using relevance feedback to get interactive image retrieval. In this paper, SVM (support vector machine) and AdaBoost are used to learn the boundary to separate the positive and negative examples.

4 2. Motivation of our approach Similarity measure is a key component in image retrieval. Traditionally, Euclidean distances are used.  Two problems with hyper-sphere query. We propose an irregular nonsphere boundary to enclose the similar images and using Euclidean distances inside the boundary. The other way: Using the distances of the images to the boundary?

5 2. Motivation of our approach (con.)

6 3. Constrained similarity measure Learning boundary with SVM: Lagrange function:

7 3-1 Learning with SVM

8 Transformed to its dual problem. The solution is:

9 3-1 Learning with SVM Kernel functions are for nonlinear mapping. Using Gaussian radial basis function (GRBF).

Learning with SVM

11 3. Constrained similarity measure (con.)

12 3. Constrained similarity measure (con.) Constrained similarity measure (CSM): Using Euclidean distance to deal with the positive images. Using the distance-from-boundary (DFB) measure to deal with the negative images.

13

14 4. Experimental results Image database: Corel photo gallery image database. We select nine concepts. The number of returning images is 40.

15

16

17

18 5. Discussion and future research 1.Feature selection:

19 5. Discussion and future research (con.) 2. Reducing the number of support vectors. 3. Is SVM always better than AbaBoost?