Content-Based Image Retrieval (CBIR) systems employ colour as primary feature with texture and shape as secondary features. In this project a simple, image retrieval system will be implemented

1. Texture and Color based image
retrieval
Arzoo kazi-11
Aatif momin-27
Rinki nag-38
Guide :
Er.Zafar khan
Presented by :
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2. Color based Literature survey
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Colour Feature Pros Cons
Conventional Color Histo
gram
-Simple
-Fast Computation
-High dimensionality
-No color similarity
-No spatial info
Fuzzy Color Histogram -Fast Computation
-Encodes color similarity
-Robust to quantization
noise
-Robust to change in
constrast
-High dimensionality
-More computation
-Appropriate choice of
membership needed
Color Correlogram -Encodes spatial info -Very slow computation
-High dimensionality
-Donot encodes color
similarity
Color--
Shape Based Method
-Encodes spatial info
-Encodes area
-Encodes shape
-More computation
-Sensitive to clutter
-Choice of appropriate
color quantization
thresholds needed

3. Texture based Literature survey:
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Texture Features Pros Cons
Steerable Pyramid Support any number of orientation Subband undecimated hence more
computation and storage
Contourlet Transform Lower Subband decimated Number of orientation supported needs
to be power of 2
Gabor Wavelet Transform Achieve highest retrieval result Result in over-complete representation
of image.
Computationally intentive
Complex Directional Filter Bank Competative retrival result Computationally intentive

4. Problem definition
• Traditional methods of image retrieval are based on associated
metadata such as keywords and text.
• The traditional metadata based image retrieval may suffer from
several critical problems, such as, the lack of appropriate metadata
associated with images, incorrect metadata.
• Limitation of characters in the keywords to express the visual content
of the image.
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5. Problem Solution
1) Instead of manual typing keywords its better and efficient to search
with images in a large database as keywords may not capture every
details which is plus point for image based search .
2) Thus we will build a system that can filter images based on their color
and texture .
3) For color retrival we are using HSV with CCV and for texture GLCM
algorithms.
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6. Working of CBIR
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7. How image search works?
• Color Retrieval
• Texture Retrieval
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8. Color Retrieval:
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9. Texture Retrieval :
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10. Performance measurement
parameter:
• To evaluate the retrieval efficiency of the proposed system, we use the
performance measurefor color is histogram euclidean distance
,histogram intersection distance .
• For texture parameters are constrast,homogeneity,energy & corelation.
• Precision= Number of relevant images retrieved / Total number of
images retrieved
• Recall= Number of relevant images retrieved / Total number of relevant
images
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11. Applications of CBIR:
1) Art Collections Example: - Fine Arts Museum of San Francisco
2) Medical Image Databases Example:-CT, MRI, Ultrasound,
3) Scientific Databases Example:-Earth Sciences
4) General Image Collections for Licensing
5) Architectural and engineering design
6) Fashion and publishing
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12. Future Scope:
1) Increasing retrieval performance.
2) Fine-tuning may be done adding some shape and structure
3) Finger print recognition, retina identification, object detection, etc for
large image databases.
4) There is a scope for time optimization also.
5) Extend this in web based applications.
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13. References
1) Khutwad, Harshada Anand, and Ravindra Jinadatta Vaidya. "Content Based Image
Retrieval." International Journal of Image Processing and Vision Sciences (ISSN Print:
2278 – 1110) Vol 2, no. 1 (2013).
2) Singh, Garima, and Priyanka Bansal Minu. "Content Based Image Retrieval."
International Journal of Innovative Research and Studies (ISSN: 2319-9725) Vol 2, no. 7
(July 2013).
3) Singha, Manimala, and K Hemachandran. "Content Based Image Retrieval using Color
and Texture." Signal & Image Processing : An International Journal (SIPIJ) Vol 3, no. 1
(2012).
4) Kodituwakku, Saluka Ranasinghe, and S Selvarajah. "Analysis and Comparison of
Texture Features for Content Based Image Retrieval." International Journal of Latest
Trends in Computing (E-ISSN: 2045-5364) Vol 2, no. 1 (March 2011).
5) Kaur, Simardeep, and V K Banga. "Content Based Image Retrieval." International
Conference on Advances in Electrical and Electronics Engineering, 2011.
6) Kato, Toshikazu. "Database architecture for content-based image retrieval."
Proceedings of SPIE Image Storage and Retrieval Systems. 1992.
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