Relevance Feedback: New Trends Derive global optimization methods: More computationally robust Consider the correlation between different attributes Incorporate semantic information: Text, key words Other techniques: Query expansion Store feedback information
New Trends: Global Optimization The key point is to derive a general distance function. Traditional Way: using traditional ellipses distance function aligned with the coordinate axis. Global Optimization: using general ellipses distance function that is not necessarily aligned with the coordinate axis. Therefore, it allows for correlations between attributes in addition to different weight on each component.
New Trends: Global Optimization -- Traditional Way: MARS1997 Incompleteness of MARS1997: Directly applied Rocchio’s formula and used some heuristics (such as β and γ in Rocchio’s formula). The similarity values are in fact the weighted Euclidean distance function which has ellipses whose major axis must be aligned with the coordinate axis. The weight adjustment based on standard deviation method cannot be proved optimal.
New Trends: Global Optimization -- More Robust Method: MindReader Robustness of MindReader: Directly go for an optimal solution for minimum problem in “hidden distance” without using some heuristics. Allow not only for different weights of each attribute, but also for correlations between attributes. In the general ellipses distance function, the major axis are not necessarily aligned with the coordinate axis.
New Trends: Global Optimization -- More Robust Method: MindReader Different distance functions:
New Trends: Global Optimization -- More Robust Method: MARS1999 Improvement in MARS1999: combine the two best-known techniques of RF (MindReader and MARS) to overcome the shortcomings that each technique has. Takes into account the multi-level image model. Derives the optimal solutions for both the query vectors and the weights associated with the multi- level image model.
New Trends: Combine Semantic Info. Problems in previous systems: Only perform feedback at the low-level feature vector level, and fail to take into account the actual semantics information for the images. Solution: Embed semantic information into the low-level feature based image retrieval system.
New Trends: Combine Semantic Info. --a framework : Ifind The basic idea: construct a semantic network and integrated it with low-level feature vector based relevance feedback by using a modified form of the Rocchio’s formula
Other New Trends Query Expansion: In each iteration of feedback, the relevant objects are added to the query and non-relevant ones are removed. Store feedback information: Store the outcome of a feedback process when the process is terminated. The stored parameters can be used to predict the parameter settings for similar queries by interpolation.
Conclusion Relevance Feedback is an excellent technique for improving the retrieval effectiveness. Problems with RF: Low-level features cannot sufficiently represent the content of image. To embed semantic information into the systems still has trouble dealing with the issues of weight normalization, thresholds selection and scalability. No state-of-art systems can provide the object level queries. It is also a big research issue in content- based retrieval society.