Transcript Relevance Feedback in Image Retrieval System: A Survey Tao

Relevance Feedback in
Image Retrieval System:
A Survey
Tao Huang
Lin Luo
Chengcui Zhang
Outline
Introduction
 Background
 Relevance Feedback Techniques
Current Research Work
 Multi-level Image Object Model
 System Examples
Promising Directions
 Global Optimization Methods
 Semantic Information Incorporation Methods
Background - (1)
The need of Image Retrieval
There has been an explosion in the quantity and
complexity of multimedia data in recent years due to:
 the development of digital technique ;
 the advance of computer technologies;
 the widespread of network systems.
The need for tools and systems to manage
multimedia data is greater than ever, especially the
management and retrieval of images.
Background - (2)
Text-based Information Retrieval Approaches
Text-based information retrieval approaches are the
most conventional approaches.
Queries are performed on document surrogates such as
keywords, titles and abstracts.
Such traditional approaches have also been applied for
image data.
Background (3)
Keyword Annotation Image Retrieval
Images are annotated manually by keywords and be
retrieved by their corresponding annotation.
Major drawbacks of this approach:
The vast amount of labor required
in manual image annotation
The differences in interpretation of
image content
 Inconsistency of image annotation
among different indexers
Background (4)
Content-based Image Retrieval Approaches
Content-based retrieval approaches use numerical
features computed by direct analysis of the image content
such as color, shape, texture.
This approach is favorable for:
 Features can be computed automatically;
 Information used during the retrieval process is always
consistent.
Background (5)
CBIR System
CBIR stands for Content-base Image Retrieval.
CBIR systems use visual features of images like
texture, color, shape, line orientation, …, to represent the
image content.
Data objects in CBIR systems are thus represented by
feature vectors and retrieval is performed on computing
similarity in the feature space.
The similarity between different images is typically
defined using the distance between image points in a
multi-dimensional feature space.
Background (6)
General Retrieval Model for CBIR
Retrieval
model
query model:
Q  {q1 , q2 ,...,qt }
image model:
D  {d1 , d2 ,...,dt }
similarity model: S  {q1 , q2 ,...,qt }
Background (7)
Design Strategy of CBIR System
 Find the “best” representation for the visual
features;
 Then,
user selects visual feature(s);
 System tries to find the similar images
Relevance Feedback Technique - (1)
Limitation of CBIR Approaches
Two distinct characteristics of CBIR system limit
their usefulness:
 gap between low-level features and high-level
concepts
 subjectivity of human perception of visual
content
Relevance Feedback - (2)
Basic Idea of Relevance Feedback
Relevance feedback is a technique of query
refinement.
Major feature: user is incorporated as part of the
image retrieval loop.
Advantage:
• remove weight-specifying burden from user
• establish more accurate link between lowlevel features and high-level concept
• better the retrieval performance of system
Relevance Feedback - (3)
Steps of Relevance Feedback
(1) System provides a group of images according
to user’s query;
(2) Users feed back negative/positive images
information;
(3) System learns from user’s feedback;
(4) System then refines the original query;
(5) repeats steps (1) ~ (4) till user is satisfied.
Relevance Feedback - (4)
Formula for Relevance Feedback
1
Q  Q   (
N rel
'
1
Di )   (

N non
rel
D )
i
non