This document discusses various approaches to image indexing and retrieval, including using text descriptions, extracting color, shape and texture features, compressed image data, and spatial relationships. It describes common techniques like color histograms, shape representations, texture features, and using DCT, wavelet or VQ compression. An integrated approach is recommended to support both textual queries and pictorial similarity comparisons.

1. Image Indexing and Retrieval
Rachmat Wahid Saleh Insani, S.Kom
Multimedia Database Management System - Chapter 6

2. Objectives
• Image indexing and retrieval approaches.
• Image retrieval based on text description.
• Image Indexing and Retrieval based on features representation
(color, shape, and texture).
• Image similarity calculation.
• Image indexing and retrieval techniques based on compressed
image data.
• Other image indexing and retrieval technique.
• Integrated image retrieval technique.
Multimedia Database Management System - Chapter 6

3. Image Indexing And
Retrieval Approaches
Multimedia Database Management System - Chapter 6

4. Image Indexing and Retrieval
Approaches
First approach: a set of attributes.
Multimedia Database Management System - Chapter 6

5. Image Indexing and Retrieval
Approaches
Second approach: An integrated feature-extraction/
object-recognition subsystem.
Multimedia Database Management System - Chapter 6

6. Image Indexing and Retrieval
Approaches
Third approach: image annotation.
Multimedia Database Management System - Chapter 6

7. Image Indexing and Retrieval
Approaches
Fourth approach: low level image features.
Multimedia Database Management System - Chapter 6

8. Image Retrieval Based
On Text Description
Multimedia Database Management System - Chapter 6

9. Text Based Image Retrieval
Multimedia Database Management System - Chapter 6

10. Color Based Indexing and
Retrieval Technique
• The idea is to retrieve from database image that has perceptually
similar colour to the user's query image or description.
• During retrieval, the distance between the histogram of the query
image and image in the database are measured.
• A color histogram H(M) is a vector (h1, …, hj, … hn).
• hj, number of pixel of image M falling into bin j.
• hn, number of pixel of image M falling into all bin.
• Bin, is a discrete color combinations.
Multimedia Database Management System - Chapter 6

11. Color Based Indexing and
Retrieval Technique
The simplest distance between images I and H is the L-1
metric, defined as
nΣ
d(I,H) = | il − hl |
l=1
Example: We have three images of 8x8 pixels and each
pixel is in one of eight colors C1 to C8. Image 1 has 8
pixels in each of the eight colors, Image 2 has 7 pixels in
each of colors C1 to C4, and 9 pixels in each of colors C5
to C8. Image 3 has 2 pixels in each of colors C1 and C2,
and 10 pixels in each of colors C3 to C8. Which two images
are most similar and which two images are most different?
Multimedia Database Management System - Chapter 6

12. Improvements to the Basic
Technique of Color Based IR
• Making use of similarity among colors.
• Making use of spatial relationships among pixels.
• Making use of the statistics of color distribution.
• Better color representation.
Multimedia Database Management System - Chapter 6

13. Image Retrieval Based on
Shape
• Images are segmented into individual objects.
• The basic issue is shape representation and similarity measurement
between shape representations.
• A good shape representation and similarity measurement for recognition
and retrieval purposes have important properties:
- Each shape should have a unique representation, invariant to
translation, rotation, and scale;
- Similar shapes should have similar representations so that retrieval can
be based on distances among shape representations.
• The similarity measure between shape representations should conform to
human perception.
Multimedia Database Management System - Chapter 6

14. Image Retrieval Based on
Texture
• Texture is described by six features:
- coarseness, opposite to fine;
- contrast, dynamic range of gray level, ratio of bow areas,
sharpness of edges, and period of repeating pattern;
- directionality, element shape and placement;
- line likeness, shape of a texture element;
- regularity, variation of an element placement rule;
- roughness.the texture is rough or smooth.
Multimedia Database Management System - Chapter 6

15. Image Indexing and Retrieval
Based on Compressed Image Data
• There are three common compression technique
for image indexing and retrieval:
- DCT Coefficient
- Wavelet Coefficient
- VQ Compressed Data
Multimedia Database Management System - Chapter 6

16. Other Techniques
• Image Retrieval Based on Model-Based
Compression
• Image Retrieval Based on Spatial Relationship
Multimedia Database Management System - Chapter 6

17. Image Retrieval based on
Model-based Compression
• An object, is represented by a mathematical model
(parameter or mathematical equation).
• A very little data required for representing these
parameters and equations, so a very high
compression can be achieved.
• Image distance calculated by parameters
differences.
Multimedia Database Management System - Chapter 6

18. Image Retrieval Based on
Spatial Relationship
• A spatial relationship,
specifies how some object is
located in space in relation
to some reference object.
• Example queries, “find
images containing a sun
above to a mountain”.
• Example application,
Geographical Information
System (GIS).
Multimedia Database Management System - Chapter 6

19. Integrated Image Indexing
and Retrieval Techniques
Structured
attributes Pictorial queries
are not supported
Integrated IR
Techniques +
relevance
feedback
Text-annotation
Color based High-level
abstractions in
images are not
supported
Shape based
Texture based
Multimedia Database Management System - Chapter 6