# Relevance Feedback: New Trends - PowerPoint PPT Presentation

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## Relevance Feedback: New Trends

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### Relevance Feedback: New Trends Derive global optimization methods: More computationally robust Consider the correlation between different attributes – PowerPoint PPT presentation

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Title: Relevance Feedback: New Trends

1
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

2
New Trends Global Optimization
• The key point is to derive a general distance
function.
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.

3
New Trends Global Optimization--Traditional
Way MARS1997
• Incompleteness of MARS1997
• Directly applied Rocchios formula and used some
heuristics (such as ß and ? in Rocchios
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.

4
New Trends Global Optimization--More Robust
• 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.

5
New Trends Global Optimization--More Robust
• Different distance functions

6
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.

7
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.

8
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 Rocchios formula

9
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.

10
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.