cbir for sketches in image processing-1001


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 Efficient Image Retrieval Using Indexing Technique
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C TT II, C EE
 two features are very powerful features for CBIR systems, especially sketch-based image  in a
personal digital Future work of this project is CBIR Systems prove  Euclidean Embedding for
Content- Based Image Retrieval, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 

 Idea Sketch of Searchable Image Encryption System on Streaming Media
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BR Cha, NH Kim, JH Seo, JW Kim ,icserv.kjist.ac.kr
 In this paper, we sketch the idea of searchable image encryption system to provide the  Keywords:
Searchable Image Encryption System, Streaming Media, Image Keyword, CBIR  In this paper,
we propose the searchable image encryption system (SIES) on streaming media of 

 CONTENT BASED MEDICAL IMAGE RETRIEVAL USING TEXTURE DESCRIPTOR
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A Gupta, S Khurana ,euroasiapub.org
Retrieved image using Query by sketch  namely, the retrieval
of images that look like that sketch. Both the paradigms involve usually  Generally, the goal and
paradigms of the CBIR are application-dependent. For example, it 

 Implementation of CBIR Method and its Architecture Implementation of CBIR Method and its Architecture
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PG Sandhya
 o Submits his own sketch of brain as query. • The system will extract image features for  The CBIR
system GUI editor menu is shown in the SS: 5. The modules in the CBIR system are Image search
based on the text (that is name of the image can be given for the search process 

 Content Based Image Retrieval: Review
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DS Shete, MS Chavan ,ijetae.com
 Query by sketch [45] and query by example [46] is to draw a sketch or provide an example image
from which images with similar visual features will be extracted from the database.  [6 ] Cbir method
based on color-spatial feature, Zhang Lei  Image Processing, vol. 6, no. 2, 1997. 

 Content Based Image Retrieval Methods Using Graphical Image Retrieval Algorithm (GIRA)
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P Jayaprabha, R Somasundaram ,International Journal of Information ,researchpub.org
 QBIC ,Query By Image Content system, developed by IBM, makes visual content similarity  The
query can either be example images, user-constructed sketches and drawings or selected  images
[8]. Virage 35] and Excalibur are other developers of commercial CBIR systems. 

 Open-end Image Retrieval System via Real Cultural Resources
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M Hayashi, K Motoyama ,inter-disciplinary.net
 In the framework of extracting shape information from images, there are three levels: first early
vision, second the sketch of 2.5 dimensions, and third the three  3. Information Reduction in the
Process of Image Retrieval Content Based Image Retrieval (CBIR) is an 

 Semantic fuzzy Colour Algorithm for Image Retrieval Using Hindi Dialects
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MT Student
 A. Feature Extraction CBIR operates on a collection by comparing features automatically  QBIC
supports queries based on example images, user-constructed sketches and drawings  and
combining these with image analysis and comparison techniques from image processing. 

 Content Based Image Retrieval Methods Using Random Segmentation Algorithm
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C TT II, C EE
 QBIC ,Query By Image Content system, developed by IBM, makes visual content similarity
comparisons  The query can either be example images, user-constructed sketches and drawings
or  images [8]. Virago 35] and Excalibur are other developers of commercial CBIR systems 

 Augmented Content Based Image Retrieval using Fusion Based on Self Organizing Map
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R Priya, VK David ,ijirset.com
 A CBIR system is defined as a task which uses an amalgamation of techniques, like statistics,
image processing and pattern recognition, with the aim of searching and retrieving images from
a large image  The query can either be an example image or a sketch, text or terms of 

Efficient similarity search on 3D bounding box annotations
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 In an application scenario, a physician might sketch an MBR around a certain type of ROI  In this
paper, we proposed an efficient retrieval framework for CBIR on three  H. and Deserno, TM,
Content-based medical image retrieval, in [Biomedical Image Processing], Deserno, TM 

 Impact on Image Retrieval with Successive Truncation of DCT
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NST Sai ,research.ijais.org
 [13] NST Sai, Ravindra patil, Moments of Pixel Distribution of CBIR International Conference 
Wang, G. Wiederhold, O. Firschein, and SX Wei, Wavelet Based Image Indexing Techniques
with Partial Sketch Retrieval Capability  [32] Pratt WK, Digital image processing, A Wiley 

INTERFACING GPS SYSTEM WITH CONTENT BASED IMAGE RETRIVAL FOR DETECTION OF ELECTRONIC OBJECTS
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SR Bommireddy ,Advances in Electrical Engineering , 2012 ,worldsciencepublisher.org
 the entire image), querying by multiple example images, querying by visual sketch, querying
by  CBIR systems can also make use of relevance feedback, where the user progressively  5. RC
Gonzalez, RC Woods, Digital Image Processing, Addison- Wesley, Reading, MA, 1992. 

 Feature Extraction Approach for Content Based Image Retrieval
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PP Lokhande, PA Tijare ,International Journal, 2012
 Research and development issues in CBIR cover a range of topics, many shared with mainstream
image processing and information retrieval.  over the candidate image’s shape located in the same
relative position as the query contour, if the sketch contains k contours. 

Dynamic two-stage image retrieval from large multimedia databases
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 For CBIR, we used the JCD, SpCD, and TOP-SURF, separately, as well as the late fusion setup
of JCD and SpCD as in Eq.  (2009), where the authors state that Web image retrieval by text queries
is often noisy and employ image processing techniques in order to re-rank 

COLOR CORRELOGRAM AND ENERGY LEVELS FOR IMAGE RETRIEVAL
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SR Bommireddy, R Koduri ,Advances in Computational , 2012 ,worldsciencepublisher.org
 by image region (rather than the entire image), querying by multiple example images, querying
by visual sketch, querying by direct specification of image features, and  CBIR systems can also
make use of relevance feedback, where the user progressively refines the search 

 Integration of Histogram Insertion Measure Method for Achieve Effecient Retreival of Images
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MV Indrani, MK Prathima, MB Vikas, MC Roopasree ,ijmer.com
 3443 | Page by submitting an example of the type of image being sought, though some offer
alternatives such as selection from a palette or sketch input.  Fig.3: Approaches of CBIR  In
International Conference on Image Processing (ICIP), pages 568– 571, Oct. 1997. 

[BOOK] Perception and Machine Intelligence: First Indo-Japan Conference, PerMIn 2012, Kolkata, India, January 12-13, 2011, Proceedings
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 Amita Chatterjee Face Image Retrieval Based on Probe Sketch Using SIFT  Kundu Fast
Computation of Edge Model Representation for Image Sequence Super  Ghosh SLAR
(Simultaneous Localization And Recognition) Framework for Smart CBIR.. 

 Complementary Relevance Feedback Methods for Content ? Based Image Retrieval
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PY Yin, CM Chen ,cdn.intechweb.org
 Users access CBIR systems (FlicknerSawhney, 1995; Pentland et al., 1996; Rui et al  1998;
Mokhtarian et al., 1996) by directly submitting image examples, object sketches, or other  color,
shape, texture, etc.) The retrievals are ranked based on image processing and similarity 

Comparison of Color, Texture and ICM Features in CBIR System
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S Bhadoria, M Madugunki, CG Dethe ,Advanced Materials , 2012 ,Trans Tech Publ
 to their similarities between the feature vectors of the query example or sketch and those of  The
main Challenge in front of the CBIR system is time Complexity and to design the  and S. Sural,
Characteristics of weighted feature vector in content-based image retrieval applications 

 THE INFLUENCE OF THE SIMILARITY MEASURE TO RELEVANCE FEEDBACK
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 A. Boucher, Text Retrieval Relevance Feedback Techniques for Bag- of-Words Model in CBIR,
International Conference  S. Beck- man, Relevance Feedback: a Power Tool for Interactive
Content-Based Image Retrieval, IEEE Trans.  [12] S. Liang, Z. Sun, Sketch Retrieval and 

 Automated Colorization of Grayscale Images Using Texture Descriptors and a Modified Fuzzy C-Means Clustering
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C Gauge, S Sasi ,Journal of Intelligent Learning Systems and , 2012 ,scirp.org
 Content-Based Image Retrieval (CBIR) Content-Based Image Retrieval (CBIR) is the ability to
retrieve images by analyzing their content.  edges play an important role for image percep- tion,
they can be used for image-to-image matching (by example or by sketch) to retrieve 





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