A novel log-based relevance feedback technique in content- based image retrieval Reporter: 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