The document discusses content-based image retrieval (CBIR). It defines CBIR as retrieving images from a collection based on automatically extracted features like color, texture, and shape. The document outlines the history and motivation for CBIR. It discusses features used for retrieval like color, texture, shape. Filtering algorithms and clustering methods used for CBIR are also summarized. Applications of CBIR include medical imaging, stock photography, and military intelligence. CBIR is presented as an effective alternative to text-based image retrieval.

1. PRESENTED BY
RAJASEKAR G
3rd MCA
Madras University
CONTENT BASED
IMAGE RETRIEVAL
(CBIR)

2. Introduction
Definition
Features of image
History of image retrieval
Filtering in image
Clustering Algorithm
Conclusion

3. 1 Content-based image retrieval (CBIR) systems are capable to use
query for visually related images by identifying similarity between a
query Image and those in the image database.
2
3
4
5
INTRODUCTION OF CBIR
Content Based Image Retrieval (CBIR) is still a research area,
which aims to retrieve images based on the content of the query
image.
The results are the images that its features are most similar to the
query image.
we have proposed a CBIR based image retrieval system, which
analyses innate properties of an image such as, the color, texture, and
histogram for efficient and meaningful image retrieval.
The system first extracts and stores the features of the query image
then it go through all images in the database and extract the
features of each image.

4. Definition:
“The process of retrieving images
from a collection on the basis of
features (such as colour, texture and
shape) automatically extracted from
the images themselves”

5. COLOR TEXTURE
•Histograms, Gridded layout,
wavelets.
•Spectrum that covers visible colors :
400 ~ 700 nm
•Radiance, Luminance, Brightness
•An image texture is a set of metrics
calculated in image processing
designed to quantify the perceived
texture of an image.
•Region based segment
•Boundary based Segment
SHAPE OTHER RELATED OBJECTS
Two dimensional and Three dimensional
. External representations(edge and line
detection).
Internal representations.
Use of the object boundary
and its
Features (e.g. boundary
length)

7. Motivation
To efficiently search/retrieve
relevant information that people
want to use
Goal
To make it easy to
search/retrieve/filter/exchange
content to maintain archive, and to
edit multimedia content etc.

8. Image Retrieval from the image collections
involved with the following steps :
1 Pre-processing
2 Image Classification based on some true factor
3 RGB Components processing
4 Pre-clustering

9. 5 Texture feature extraction
6 Similarity comparison
7 Target image selection
8 Target image is retrieved

11. Image
Color Shape
Color Shape
Server
Internet
or
Intranet
or
Extranet
Query Interface
Client
Query by
Color
Query by
Color Sensation
Query by
Shape
Learning
Mechanism
Query by
Images
User Drawing
Query by
Spatial Relation
Weight of Features
Fectures Extraction
Color Sensation
Spatial Relation
Similarity Measure
Color Sensation
Spatial Relation
Indexing
&
Filtering Image Database
Query
Server

12. Traditional text-based image search engines
Manual annotation of images
Use text-based retrieval methods
E.g. Water lilies
Flowers in a pond
<Its biological
name>

13. by google
by yahoo etc..

14. Narrow vs. Broad Domain
Narrow
Medical Imagery Retrieval
Finger Print Retrieval
Satellite Imagery Retrieval
Broad
Photo Collections
Internet

16. One of the most important factors that
greatly affect the quality of clinical nuclear
medicine images is image filtering.
Image filtering is a mathematical processing
for noise removal and resolution recovery.
The goal of the filtering is to compensate for
loss of detail in an image while reducing
noise.

17. Mean Filter :
Mean filter is the simplest low pass linear filter.
It is implemented by replacing each pixel
value with the average value of its
neighbourhood. Mean filter can be
considered as a convolution filter.

18. Median Filter:
Median filter is a non linear filter. Median
filtering is done by replacing the central pixel
with the median of all the pixels value in the
current neighbourhood.

19. Gaussian Filter:
Gaussian filter is a linear low pass filter. A
Gaussian filter mask has the form of a bell
shaped curve with a high point in the centre
and symmetrically tapering

20. Clustering is a method of grouping data objects into
different groups, such that similar data objects belong to
the same group and dissimilar data objects to different
clusters.
Image clustering consists of two steps the former is
feature extraction and second part is grouping.
Clustering algorithm is applied over this extracted
feature to form the group.

22. Planning and government: there is a lot of satellite
imagery of the earth, which can be used to inform important
political debates.
Military intelligence: satellite imagery can contain
important military information. Typical queries involve finding
militarily interesting changes — for example, is there a
concentration of force? how much damage was caused by the last
bombing raid? what happened today? etc. — occurring at
particular places on the earth

23. Stock photo and stock footage: commercial libraries —
which often have extremely large and very diverse collections —
survive by selling the rights to use particular images. Effective tools
may unlock value in these collections by making it possible for
relatively unsophisticated users to obtain images that are useful to
them at acceptable expense in time and money.
Access to museums: museums are increasingly creating
web views of their collections, typically at restricted resolutions, to
entice viewers into visiting the museum. Ideally, one would want
viewers to get a sense of what is at the museum, why it is worth
visiting and the particular virtues of the museum’s gift store.
Trademark and copyright enforcement: as electronic
commerce grows, so does the opportunity for automatic searches to
do violations of trademark or of copyright. For example, at time of
writing, the owner of rights to a picture could register it with an
organization called BayTSP, who would then search for stolen copies
of the picture on the web; recent changes in copyright law make it
relatively easy to recover fines from violators (see
http://www.baytsp.com/index.asp).

24. Managing the web: indexing web pages appears to be a
profitable activity; the images present on a web page
should give cues to the content of the page. Users may
also wish to have tools that allow them to avoid offensive
images or advertising. A number of tools have been built
to support searches for images on the web using CBIR
techniques. There are tools that check images for
potentially offensive content, both in the academic and
commercial domains.
Medical information systems: recovering medical images
“similar” to a given query example might give more
information on which to base a diagnosis or to conduct
epidemiological studies. Furthermore, one might be able
to cluster medical images in ways that suggest interesting
and novel hypotheses to experts.

26. Retrieving images based on the keywords is
not only appropriate, but also time
consuming.
When compared to TBIR, CBIR is very
effective and appropriate.
Focused on effective FEATURE
representation such as color, texture, shape.
Easy to retrieve image databases.

31. There is a need for CBIR.
It may be sufficient that a retrieval system present similar
images, similar in some user-defined sense.
CBIR has overcome all the limitation of Text Based Image
Retrieval by considering the contents or features of image.
To make it easy to search/retrieve/filter/exchange content
to maintain archive, and to edit multimedia content etc.
CBIR technology has been used and also using in several
applications such as fingerprint identification, biodiversity
information systems, digital libraries, crime prevention,
medicine, historical research.

32. Remco, C.V., Mirela, T., “Content based
image retrieval systems: a survey”.
http://www.mathworks.in/matlabcentral
http://stackoverflow.com/questions/1476836
4/algorithms-used-for-content-based-image-retrieval-
systems
Content Based Image Retrieval(CBIR)
System Based on the Clustering and
Genetic Algorithm
-Eng. Ahmed K. Mikhraq

33. That is all, folks…
Thank you for your
patience!