3. INTRODUCTION
▪ CBIR (Content-Based Image Retrieval) also known
as query by image content.
▪ CBIR is the retrieval of images based on visual
features such as colour, texture. The aim of CBIR is
to avoid the use of textual descriptions.
4. 1.
MOTIVATION
• Image databases and collections can be enormous in size, containing
hundreds, thousands or even millions of images. The conventional
method of image retrieval is searching for a keyword that would match
the descriptive keyword assigned to the image by a human
categorizer.
5. Limitations of text-based approach
▪ Problem of image annotation
▫Large volumes of databases
▪ Problem of human perception
▫Depends of human perception
▫Too much responsibility on the end-user
▪ Problem of Queries that cannot be described at all,
but tap into the visual features of images.
6. Why CBIR
▪ Impression is more by an image rather than thousands of words.
▪ The term CBIR describes the process of retrieving desired images from the
large collection of database on the basis of features that can be
automatically extracted from the image themselves.
▪ Based on color , texture , histogram
7. A picture is worth
a thousand words
A complex idea can
be conveyed with just
a single still image,
namely making it
possible to absorb
large amounts of data
quickly.
9. ▪ It involves two steps:
▪Feature Extraction: The first step in the process is extracting image
features to a distinguishable extent.
▪Matching: The second step involves matching these features to yield
a result that is visually similar.
▪In CBIR, each image that is stored in the database has its features
extracted and compared to the features of the query image.
11. • Histogram is a measure used to describe the image.
In simple words it means the distribution of color
brightness across the image. The brightness values
range in [0..255].
• Texture is defined as the tactile quality of the
surface of an object--how it feels if touched.
• Colors can be created on computer monitors with
color spaces based on the RGB color model , using
the additive primary colors (red,green, and blue).
17. ▪ It avoid the annotation of name with each image.
▪ Visual features of images are extracted automatically.
▪ Similarities of images are based on the distances between
features.
▪ It is more efficient than previous technique.
Advantages
18. • Crime prevention: Automatic face recognition systems, used by
police forces.
• Security Check: Finger print or retina scanning for access
privileges.
• Medical Diagnosis: Using CBIR in a medical database of medical
images to aid diagnosis by identifying similar past cases.
• Intellectual Property: Trademark image registration, where a new
candidate mark is compared with existing marks to ensure no risk of
confusing property ownership.
Applications of CBIR
19. CONCLUSION
▪ In this project, image colour pixel value are used as an image attribute based
method to find out minimal set of pixel value intervals for different colours. This
reduced set acts as the input to rough membership based computation to classify
different types of images.
▪ The Retrieval algorithm presented in this project mainly reduces the
computational time and at the same time increases the user interaction.
▪The results obtained are also less in number so that there is no need for the user to spend
more time in analysis.
20. References
• Kumar A., Esther J.” Comparative Study on CBIR based by Color Histogram, Gabor and
Wavelet Transform”, International Journal of Computer Applications (0975 – 8887)
Volume 17– No.3, March 2011.
• Arthi K, Vijayaraghavan J., “Content Based Image Retrieval Algorithm Using Colour
Models”, International Journal of Advanced Research in Computer and Communication
Engineering
• Pavani S., Shivani K., RaoVenkat T., “Similarity Analysis Of Images Using Content
Based ImageRetrieval System”,International Journal Of Engineering And Computer
Science
