A REVIEW ON MULTISCALE TEXTURE FEATURES USING STEERABLE PYRAMIDS
DOI:
https://doi.org/10.24297/ijct.v15i13.24Abstract
As a result of recent advancements in digital storage technology, it is now possible to create large and extensive databases of digital imagery. These collections may contain millions of images and terabytes of data. For users to make the most of these databases effective, efficient methods of searching must be devised. Having a computer do the indexing based on a CBIR scheme attempts to address the shortcomings of human-based indexing. Since a computer can process images at a much higher rate, while never tiring, the manpower issue is solved. In this paper, we will discuss the architecture of CBIR with steerable pyramids and their shortcomings.Downloads
Download data is not yet available.
References
[1]
M. Fakheri, T. Sedghi, Mahrokh G. Shayesteh and Mehdi Chehel Amirani, "Framework for image retrieval using machine learning and statistical similarity matching techniques," IET Image Process., pp. 1-11, 2013.
[2]
P. MANIPOONCHELVI and K. MUNEESWARAN, "Multi region based image retrieval system," Indian Academy of Sciences, pp. 333-344, 2014.
[3]
H. Jegou, M. Douze, C. Schmid and P. Perez, "Aggregating local descriptors into a compact image representation," IEEE, pp. 3304-3311, 2010.
[4]
Yixin Chen, James Z. Wang, and Robert Krovetz, "CLUE: Cluster-Based Retrieval of Images by Unsupervised Learning," IEEE, pp. 1181-101, 2005.
[5]
R. Fergus, L. Fei-Fei, P. Perona and A. Zisserman, "Learning Object Categories from Google’s Image Search," IEEE, 2005.
[6]
Y. Chen, X. Li, A. Dick and Anton van den Hengel, "Boosting Object Retrieval With Group Queries," IEEE, pp. 765-768, 2012.
[7]
R. Arandjelovi´c and A. Zisserman, "Three things everyone should know to improve object retrieval," IEEE, pp. 2911-2918, 2012.
[8]
M. Perdoch, Ondrej Chum and Jiri Matas, IEEE, pp. 9-16, 2009.
[9]
Savvas A. Chatzichristofis and Yiannis S. Boutalis , "CEDD: Color and Edge Directivity Descriptor: A Compact Descriptor for Image Indexing and Retrieval," Springer-Verlag Berlin Heidelberg, pp. 312-322, 2008.
[10]
Yixin Chen and James Z. Wang, "A Region-Based Duzzy Feature Matching Approach to content-Based Image Retrival," IEEE, pp. 1252-1267, 2002.
[11]
Chuen-Horng Lin, Rong-Tai Chen and Yung-Kuan Chan, "A smart content-based image retrieval system based on color and texture feature," Elsevier , 2008.
[12]
James Z. Wang, , Jia Li and Gio Wiederhold, "SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries," IEEE, pp. 947-963, 2001.
[13]
S. Gandhani, R. Bhujade and A. Sinhal, "AN IMPROVED AND EFFICIENT IMPLEMENTATION OF CBIR SYSTEM BASED ON COMBINED FEATURES," IET, pp. 353-359.
[14]
S. Muhammad Hayat Khan, . D. Fakhri and Dr.Imad Fakhri Taha Alshaikhl, "Comparative study on Content-Based Image Retrieval (CBIR)," IEEE, pp. 61-66, 2013.
[15]
Sreedevi S. and Shinto Sebastian, "Fast Image Retrieval with Feature Levels," IEEE, 2013.
[16]
S. Ezekiel, Mark G. Alford, David Ferris, Eric Jones,, Adnan Bubalo, Mark Gorniak and Erik Blasch, "Multi-Scale Decomposition Tool for Content Based Image Retrieval," IEEE, 2013.
[17]
K. Juneja, A. Verma, S. Goel and S. Goel , "A Survey on Recent Image Indexing and Retrieval Techniques for Low-level Feature Extraction in CBIR systems," IEEE, pp. 67-72, 2015.
[18]
Khadidja BELATTAR and Sihem MOSTEFAI , "CBIR using Relevance Feedback:Comparative Analysis and Major Challenges," IEEE, pp. 317-325, 2013.
[19]
D. Jeyabharathi and D. Suruliandi , "Performance Analysis of Feature Extraction and Classification Techniques in CBIR," IEEE, pp. 1211-, 2013.
[20]
Hui Xie, Ying Ji and Yueming Lu, "An Analogy-Relevance Feedback CBIR Method Using Multiple Features," IEEE, pp. 83-86, 2013.
[21]
B. Kaur and S. Jindal, "An implementation of Feature Extraction over medical images on OPEN CV Environment".
[22]
S. Kumar, S. Jain and T. Zaveri, "PARALLEL APPROACH TO EXPEDITE MORPHOLOGICAL FEATURE EXTRACTION OFREMOTE SENSING IMAGES FOR CBIR SYSTEM," IEEE, pp. 2471-2474, 2014.
[23]
Khadidja BELATTAR and Sihem MOSTEFAI , "CBIR with RF:which Technique for which Image," IEEE, 2014.
[24]
G. Raghuwanshi and V. Tyagi, "Texture image retrieval using adaptive tetrolet transforms," Elsevier, pp. 1-8, 2015.
M. Fakheri, T. Sedghi, Mahrokh G. Shayesteh and Mehdi Chehel Amirani, "Framework for image retrieval using machine learning and statistical similarity matching techniques," IET Image Process., pp. 1-11, 2013.
[2]
P. MANIPOONCHELVI and K. MUNEESWARAN, "Multi region based image retrieval system," Indian Academy of Sciences, pp. 333-344, 2014.
[3]
H. Jegou, M. Douze, C. Schmid and P. Perez, "Aggregating local descriptors into a compact image representation," IEEE, pp. 3304-3311, 2010.
[4]
Yixin Chen, James Z. Wang, and Robert Krovetz, "CLUE: Cluster-Based Retrieval of Images by Unsupervised Learning," IEEE, pp. 1181-101, 2005.
[5]
R. Fergus, L. Fei-Fei, P. Perona and A. Zisserman, "Learning Object Categories from Google’s Image Search," IEEE, 2005.
[6]
Y. Chen, X. Li, A. Dick and Anton van den Hengel, "Boosting Object Retrieval With Group Queries," IEEE, pp. 765-768, 2012.
[7]
R. Arandjelovi´c and A. Zisserman, "Three things everyone should know to improve object retrieval," IEEE, pp. 2911-2918, 2012.
[8]
M. Perdoch, Ondrej Chum and Jiri Matas, IEEE, pp. 9-16, 2009.
[9]
Savvas A. Chatzichristofis and Yiannis S. Boutalis , "CEDD: Color and Edge Directivity Descriptor: A Compact Descriptor for Image Indexing and Retrieval," Springer-Verlag Berlin Heidelberg, pp. 312-322, 2008.
[10]
Yixin Chen and James Z. Wang, "A Region-Based Duzzy Feature Matching Approach to content-Based Image Retrival," IEEE, pp. 1252-1267, 2002.
[11]
Chuen-Horng Lin, Rong-Tai Chen and Yung-Kuan Chan, "A smart content-based image retrieval system based on color and texture feature," Elsevier , 2008.
[12]
James Z. Wang, , Jia Li and Gio Wiederhold, "SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries," IEEE, pp. 947-963, 2001.
[13]
S. Gandhani, R. Bhujade and A. Sinhal, "AN IMPROVED AND EFFICIENT IMPLEMENTATION OF CBIR SYSTEM BASED ON COMBINED FEATURES," IET, pp. 353-359.
[14]
S. Muhammad Hayat Khan, . D. Fakhri and Dr.Imad Fakhri Taha Alshaikhl, "Comparative study on Content-Based Image Retrieval (CBIR)," IEEE, pp. 61-66, 2013.
[15]
Sreedevi S. and Shinto Sebastian, "Fast Image Retrieval with Feature Levels," IEEE, 2013.
[16]
S. Ezekiel, Mark G. Alford, David Ferris, Eric Jones,, Adnan Bubalo, Mark Gorniak and Erik Blasch, "Multi-Scale Decomposition Tool for Content Based Image Retrieval," IEEE, 2013.
[17]
K. Juneja, A. Verma, S. Goel and S. Goel , "A Survey on Recent Image Indexing and Retrieval Techniques for Low-level Feature Extraction in CBIR systems," IEEE, pp. 67-72, 2015.
[18]
Khadidja BELATTAR and Sihem MOSTEFAI , "CBIR using Relevance Feedback:Comparative Analysis and Major Challenges," IEEE, pp. 317-325, 2013.
[19]
D. Jeyabharathi and D. Suruliandi , "Performance Analysis of Feature Extraction and Classification Techniques in CBIR," IEEE, pp. 1211-, 2013.
[20]
Hui Xie, Ying Ji and Yueming Lu, "An Analogy-Relevance Feedback CBIR Method Using Multiple Features," IEEE, pp. 83-86, 2013.
[21]
B. Kaur and S. Jindal, "An implementation of Feature Extraction over medical images on OPEN CV Environment".
[22]
S. Kumar, S. Jain and T. Zaveri, "PARALLEL APPROACH TO EXPEDITE MORPHOLOGICAL FEATURE EXTRACTION OFREMOTE SENSING IMAGES FOR CBIR SYSTEM," IEEE, pp. 2471-2474, 2014.
[23]
Khadidja BELATTAR and Sihem MOSTEFAI , "CBIR with RF:which Technique for which Image," IEEE, 2014.
[24]
G. Raghuwanshi and V. Tyagi, "Texture image retrieval using adaptive tetrolet transforms," Elsevier, pp. 1-8, 2015.
Downloads
Published
2016-11-30
How to Cite
Pooja, P., & Jindal, S. (2016). A REVIEW ON MULTISCALE TEXTURE FEATURES USING STEERABLE PYRAMIDS. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 15(13), 7373–7378. https://doi.org/10.24297/ijct.v15i13.24
Issue
Section
Research Articles
