Texture and Color based image retrieval

Arzoo kazi-11Aatif momin-27Rinki nag-38

Guide : Er.Zafar khan

Presented by :

Color based Literature survey

Colour Feature Pros Cons

Conventional Color Histogram

-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

Texture based Literature survey:

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

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.

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.

Working of CBIR

How image search works?

• Color Retrieval• Texture Retrieval

Color Retrieval:

Texture Retrieval :

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

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

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.

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.