content based image retreival system presentation

1. SIR PADAMPAT SINGHANIA
UNIVERSITY
A SEMINAR REPORT PRESENTATION
SUBMITTED IN PARTIAL FULFILLMENT OF THE
REQUIREMENTS FOR THE AWARD OF
THE DEGREE OF
MASTER OF TECHNOLOGY
(Computer Science Engineering)
SUBMITTED BY
Tehzeeb Sheikh
22MCS00201

2. SEMINAR REPORT
PRESENTATION
Content-based Image
Retrieval (CBIR) System

3. What is Content-Based Image
Retrieval?
Content-Based Image Retrieval (CBIR) is a way
of retrieving images from a database. In CBIR, a
user specifies a query image and gets the images
in the database similar to the query image. To find
the most similar images, CBIR compares the
content of the input image to the database images.

4. What is Content-Based Image
Retrieval?

5. Why Content-Based?
Early techniques: text-based image retrieval using
textual annotations
Disadvantages:- 1) Annotating large databases
accurately not feasible. 2) Remarks often
ambiguous due to human perception 3) Valid for a
particular language only.

6. Example of incorrect retrieval

7. METHODOLOGY
Feature Extraction (Color, Texture, Shape, Spectral)
Image Matching (query matching with database)
Steps involved in CBIR:
1) Features are extracted from each image in the database
2) Same features are extracted from the query image
3) Similarity between query features and database features
are compared
4) Images with maximum similarity are retrieved
5) Users' relevance feedback is used to refine the retrieval
results.

8. What Are Image Features?
Represent the visual characteristics of an image.
❖Low level features: example – round ball
❖High level features: example – white golf
ball/red cricket ball
 Color
 Texture
 Shape
 Others

9. COLOUR FEATURE
➢ Each pixel in an image is represented as a point in the
3-D colour space.
➢ Commonly used colour spaces: RGB (Red, Green and
Blue), HSV (Hue, Saturation and Value), etc.
➢ Commonly used color features: color histogram, color
moments
➢ Colour Correlogram incorporates spatial correlation of
colour pairs in the colour histogram

10. TEXTURE FEATURE
➢ Texture: Regular repetition of an element or pattern on
a surface.
➢ Commonly used methods:
▪ GLCM:
Gray-level co-occurrence matrix
▪ Gabor transformation:
▪ Wavelets like Discrete Wavelet Transform (DWT),
Curvelet transform, Ridgelet transform, Fast wavelet
histogram techniques (FWHT) etc.

11. SHAPE FEATURE
➢ Geometry Features
 perimeter
 area
 number of holes
 eccentricity
 symmetry, etc. of the shape.
➢ Effective descriptors: Fourier descriptors
❑ Fourier transform of the boundary pixels.
❑ Translation-invariant contour representations.
❑ Rotation invariance - by taking only amplitudes of coefficients.
❑ Scale invariance - by normalizing the coefficients.

12. IMAGE MATCHING
Similarity measurement:
Feature vectors extracted
from query image are
compared with the
features vectors of the
database by measuring
the distance Between the
vectors. Smaller distance
implies higher similarity.

13. Specific examples:
Query by Colour in QBIC

14. Specific examples:
Query by Shape in QBIC

15. Specific examples:
Query by Sketch in QBIC

16. Applications of CBIR
 Medical diagnosis
 Crime prevention
 Fashion and interior design
 Education and training
 Web searching.

17. THANK YOU