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Content-Based Image and Video
Retrieval Algorithm
Submitted By: Akshit Kumar Bum
B.E. in Information Technology
(Final Year).

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 CBIR is the application of computer vision technique to the image
retrieval problem.
 The search uses the content of the image.
 Content might be colors, shapes, textures, or any other information
derived from image itself.
INTRODUCTION TO CBIR

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 There is an amazing growth in the amount of digital image and
video data in recent years. So the efficient image retrieval
required.
 The limitations inherent in metadata-based systems.
 Old concept based approaches are time consuming and
ineffective.
NEED OF CBIR
So much of irrelevant
images
Search in a different way
So, the CBIR is Solution
Seems Interesting,
But How It Works
?????

4. HOW CBIR WORKS
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Query Image
Feature
Extraction
Initial Feedback
Retrieved Similar
Images

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Query Techniques
Different implementation of CBIR make use of different user query. i.e.
Reverse image search
Semantic Retrieval
Relevance Feedback(Human Interaction )
Iterative/Machine Learning
Feature Extraction and indexing
Extract the feature from queried image . i.e.
Based on color
Based on Shape
Based on Texture
Image indexing
CBIR TECHNIQUES

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 Provide the sample image to the CBIR system, and CBIR system
will retrieve relevant images.
QUERY TECHNIQUES
Reverse image search
CBIR SYSTEM

7. Query Techniques
Reverse image search
Uses
 Locate the Source of an image.
 Find higher resolution versions.
 Discover webpages where the image appears.
 Track down the content creator.
 Get information about an image.
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 Google images
 TinEye images
Application in popular search system

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 Semantic retrieval starts with a user making types of arguments.
i.e. “find pictures of Sachin Tendulkar” .
 User uses the possible range of color , texture, shape to express his
or her query.
 image retrieval requires human feedback in order to identity
higher-level concepts.
SEMANTIC RETRIEVAL

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 Feedback from users as “relevant” “not relevant” or “neutral” .
 Repeat the search with the new information.
 Using this, CBIR successfully understand the user intent.
RELEVANCE FEEDBACK

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images with same color histograms
 Histogram determines the color quantization.
 The comparison between images is accomplished through the
distance or similarity between the two histograms.
 Figure shows two similar images according to histograms.
Feature Extraction
Based On Color

12. Feature Extraction
color layout
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 divide the whole image into a set of sub images.
 takes the color histogram of each sub image and global
image.
 Using these histograms we can Mention the color auto, how
the colors change with distance.

13. Feature Extraction
Based On Shapes
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 Shapes Extraction includes edges, contours.
 Methods based on quantifying edge applies on query image.
 k-dimensional metric is used to match two images.
 Skeletal representations of object shape can also be used.
 For image matching suggested the use of directional
histograms of the extracted edges.

14. Feature Extraction
Based On Texture
 Texture refers to the visual patterns that have properties of
homogeneity or arrangement.
 Calculate the relative brightness of selected pairs of pixels
from each image(Query and stored images).
 The system then retrieves images with texture measures
most similar in value to the query.
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15. Image Indexing
 This is achieved by assigning descriptive
metadata in the form of keywords.
 This metadata can be used for retrieval keys.
 Allow the user to add descriptors.
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16. Matching Query to Image
 Feature Vector Representation:
 We can represent the features of an image by
vector.
 Using the various formulas we can calculate the
similarity between two images.
 Fuzzy reasoning can be implemented for
matching the images.
 Query and User’s refinement.(region of the
interest.) 16

17. Content Based Video Retrieval
Video Parsing
• Manipulation of whole video for breakdown into key
frames.
Video Indexing
• Features of these frames are extracted and indexed
based on color, shape, texture (as in image retrieval)
Video Retrieval and browsing
• Users access the database through queries or through
interactions.
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18. Applications
 Crime prevention
 The military
 Intellectual property Architectural and engineering design
 Fashion and interior design
 Web Searching
 Journalism and advertising
 Medical diagnosis
 Geographical information and remote sensing systems
 Cultural heritage
 Education and training
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19. QUERIES??
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20. Thank You
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