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Presenting by, Prashanth B R 1AR08CS035 Dept.Of CSE. AIeMS-Bidadi .

Sketch4Match – Content-based Image Retrieval System Using Sketches. Under the Guidance of, Mr Gangadhar Immadi Asst Prof, Dept of CSE AIeMS-Bidadi . Presenting by, Prashanth B R 1AR08CS035 Dept.Of CSE. AIeMS-Bidadi . Outline of Presentation. Introduction Objectives

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Presenting by, Prashanth B R 1AR08CS035 Dept.Of CSE. AIeMS-Bidadi .

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  1. Sketch4Match – Content-based Image Retrieval System Using Sketches Under the Guidance of, MrGangadharImmadi Asst Prof, Dept of CSE AIeMS-Bidadi. Presenting by, Prashanth B R 1AR08CS035 Dept.Of CSE. AIeMS-Bidadi.

  2. Outline of Presentation • Introduction • Objectives • Purpose of the system • Global structure and subsystems • Advantages • Tests and Results • Conclusion • References

  3. Introduction to Sketch4Match-Content-based Image Retrieval System Using Sketches • The Content Based Image Retrieval is one of most popular, rising research areas of the digital image processing. • Search engine tools- Google, Yahoo… etc. • text based search.. • Goal of CBIR to extract visual content like color , text, or shape. Introduces design based on a free hand sketch --SBIR • Making search more efficient hereby. • Test results show that the sketch based system allows users an intuitive access to search-tools.

  4. Introduction to Sketch4Match-Content-based Image Retrieval System Using Sketches • In today’s corporate world huge data has to be managed, processed and stored. • The growing of data storages and revolution of internet had changed the world. • Text based search. Keywords! Is this efficient. • Why we use keywords for search- • Giving unique & identifiable name to image is not difficult. • Keyword acts as the human abstraction of that particular image. • Therefore it is to develop a CBIR system, which can retrieve using sketches in frequently used databases.

  5. Introduction to Sketch4Match-Content-based Image Retrieval System Using Sketches • Drawing area to sketch the required image. • Matched images to sketch are retrieved. • Using Sketch based system can be very important and efficient in many areas of life. • Digital library. • Crime prevention. • photo sharing sites. • search engines.

  6. Objectives • This paper aims to introduce the problems and challenges concerned with design and creation of CBIR systems, which is based on free hand sketch. • Make convenient to retrieve data or images based on sketches so that even illiterates , who do not know to write text can also make use of system effectively. • Introducing this system into search engines makes corporate world and even other users bit more efficient in retrieval of data effectively.

  7. Purpose of the system. • The Purpose of the system. • Even though the measure of research in sketch-based image retrieval is more, there is no widely used SBIR system. • The goal of this paper is to develop a SBIR search engine, which with free hand sketch content can be retrieved. • The most important task is to bridge the gap between the free hand sketch and the picture.

  8. Global structure and subsystems. 1. The Global Structure of the System: Preprocessed image Feature Vector generating subsystem image Preprocessing Subsystem Displaying Subsystem Feature vector Retrieval subsystem Result Stock index Database Management subsystem

  9. Global Structure and subsystems. 2. The Preprocessing Subsystem: The system is for databases containing simple images. Input Output Preprocessing subsystem Image Processed Image

  10. Global structure and subsystems • 3. The Feature Vector Preparation Subsystem: • Here we use 3 Descriptor vectors which represents the content of the image. • Edge Histogram descriptor(EHD). • Histogram of Oriented Gradients(HOG). • Scale invariant feature transform(SIFT). • Purpose of the above descriptors, • preprocessing of free hand sketch. • Compression of free hand sketch with gallery of images. • Retrieval of matched images from the database

  11. Global structure and subsystems • 4. The Retrieval and Database Management Subsystem: • The Retrieval subsystem is used just to retrieve the matched image to the free hand sketch. • In Database Subsystem the images and their descriptors are stored and necessary mechanism for subsequent processing is provided. • This module consists of • The storage • The Retrieval • Data manipulation modules

  12. Global structure and subsystems • 5. The Display Subsystem: • Since drawings are basis of retrieval. • The Drawing surface. • The screen for retrieval of matched images.

  13. Advantages • Make convenient to retrieve data or images based on sketches so that even illiterates , who do not know to write text can also make use of system effectively. • Introducing this system into search engines makes corporate world and even other users bit more efficient in retrieval of data effectively.

  14. Tests and Results • Used Test Databases: • The system is tested with more than one sample database to obtain a more extensive description. Few used databases are: • Flicker 160 Database • Microsoft Research Cambridge Object Recognition image Database • Wang the database from corel image database. • Testing Aspects, Used Metrics: • We can evaluate the effectiveness of the system forming methods, and comparing different applied methods. This compression can be done easily through Metrics. • By applying EHD & HOG methods on these databases, we can find this system better than some systems before. So this system is more effective than the examined other systems.

  15. Conclusion: Among the objectives of this paper performed to design, implement and test a sketch-based image retrieval system. Two main aspects were taken into account. The retrieval process has to be highly interactive. The robustness of the method is essential in some degree of noise, which might also be in case of simple images. Based on the test results with many databases HOG is more better in many cases than EHD.

  16. References • [1] D. Comaniciu, and P. Meer, “Robust analysis of feature spaces: color • image segmentation,” IEEE Conference on Computer Vision and Pattern • Recognition, pp. 750–755, June 1997. • [2] N. Dalal, and B. Triggs, “Histograms of oriented gradients for human • detection,” IEEE Conference on Computer Vision and Pattern Recognition, • pp. 886–893, July 2005. • [3] T. Deselaers, D. Keysers, and H. Ney, “Features for image retrieval: • an experimentalcomparison,” Information Retrieval, vol. 11, pp. 77–107, • December 2007. • [4] M. Eitz, K. Hildebrand, T. Boubekeur, and M. Alexa, “An evaluation • of descriptors for large-scale image retrieval from sketched feature lines,” • Computers and Graphics, vol. 34, pp. 482–498, October 2010. • [5] R. Fabbri, L.D.F. Costa, J.C. Torelli, and O.M. Bruno, “2D Euclidean • distance transform algorithms: a comparative survey,” ACM Computing • Surveys, vol. 44, pp. 1–44, February 2008. • [6] M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Hiang, B. Dom, M. • Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker, “Query • by image and video content: the QBIC system,” IEEE Computer, vol. 28, • pp. 23–32, 2002.

  17. QUERIES ?

  18. Thank you

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