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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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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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