A novel log-based relevance feedback technique in content-based image retrievalReporter: Francis

2005/6/2

2

Outline

1. Introduction

2. Log-based relevance feedback

3. Support vector machines

4. Log-based relevance feedback using SLSVM

5. Experiment results

3

1. Introduction

CBIR’s research:

1. Feature analysis and similarity measure: Semantic gap between features and human

perceptions.

2. Building the image Indexing with textual descriptions.

3. Relevance feedback

4

2. Log-based relevance feedback

Traditional approach:Query expansion (QEX)[6]: It’s good for

document retrieval but poor in image retrieval. Log-based relevance feedback:

Relevance matrix (RM)Defining correlations between images

5

2. Log-based relevance feedback

One given example may be with different relationship value confidence degrees

6

3. Support vector machines

Optimization problem formula:

7

4.1 Soft label support vector machine 22

8

4.1 Soft label support vector machine Decision function:

9

4.2 LRF algorithm by SLSVM

Training example selection:Using R(i,j)

Adding N’ positive and negative training example ranking by S+ and S-.

10

5. Experiment results

1

11

5. Experiment results

1

12

5. Experiment results