GLIBP: Gradual Locality Integration of Binary Patterns for Scene Images Retrieval
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We propose an enhanced version of the local binary pattern (LBP) operator for texture extraction in images inthe context of image retrieval. The novelty of our proposal is based on the observation that the LBP exploitsonly the lowest kind of local information through the global histogram. However, such global Histogramsreflect only the statistical distribution of the various LBP codes in the image. The block based LBP, which useslocal histograms of the LBP, was one of few tentative to catch higher level textural information. We believe thatimportant local and useful information in between the two levels is just ignored by the two schemas. The newlydeveloped method: gradual locality integration of binary patterns (GLIBP) is a novel attempt to catch as muchlocal information as possible, in a gradual fashion. Indeed, GLIBP aggregates the texture features present ingrayscale images extracted by LBP through a complex structure. The used framework is comprised of amultitude of ellipse-shaped regions that are arranged in circular-concentric forms of increasing size. Theframework of ellipses is in fact derived from a simple parameterized generator. In addition, the elliptic formsallow targeting texture directionality, which is a very useful property in texture characterization. In addition,the general framework of ellipses allows for taking into account the spatial information (specifically rotation). The effectiveness of GLIBP was investigated on the Corel-1K (Wang) dataset. It was also compared topublished works including the very effective DLEP. Results show significant higher or comparable performanceof GLIBP with regard to the other methods, which qualifies it as a good tool for scene images retrieval.
Abstract
We propose an enhanced version of the local binary pattern (LBP) operator for texture extraction in images inthe context of image retrieval. The novelty of our proposal is based on the observation that the LBP exploitsonly the lowest kind of local information through the global histogram. However, such global Histogramsreflect only the statistical distribution of the various LBP codes in the image. The block based LBP, which useslocal histograms of the LBP, was one of few tentative to catch higher level textural information. We believe thatimportant local and useful information in between the two levels is just ignored by the two schemas. The newlydeveloped method: gradual locality integration of binary patterns (GLIBP) is a novel attempt to catch as muchlocal information as possible, in a gradual fashion. Indeed, GLIBP aggregates the texture features present ingrayscale images extracted by LBP through a complex structure. The used framework is comprised of amultitude of ellipse-shaped regions that are arranged in circular-concentric forms of increasing size. Theframework of ellipses is in fact derived from a simple parameterized generator. In addition, the elliptic formsallow targeting texture directionality, which is a very useful property in texture characterization. In addition,the general framework of ellipses allows for taking into account the spatial information (specifically rotation). The effectiveness of GLIBP was investigated on the Corel-1K (Wang) dataset. It was also compared topublished works including the very effective DLEP. Results show significant higher or comparable performanceof GLIBP with regard to the other methods, which qualifies it as a good tool for scene images retrieval.
참고문헌 (35)
- A. W. M. Smeulders, "Content-based image retrieval at the end of the early years", IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12), pp.1349~1380 (2000)[DOI]
- M. J. Swain, "Color indexing", International Journal of Computer Vision, 7(1), pp.11~32 (1991)[DOI]
- M. A. Stricker, "Similarity of color images", Proceedings of the SPIE 2420: Storage and Retrieval for Image and Video Databases III, pp.381~393 (1995), International Society for Optics and Photonics
- G. Pass, "Histogram refinement for content-based image retrieval", Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision, pp.96~100 (1996)
- R. M. Haralick, "Statistical and structural approaches to texture", Proceeding of the IEEE, 67(5), pp.786~804 (1979)[DOI]
- T. Ojala, "A comparative study of texture measures with classification based on feature distributions", Pattern Recognition, 29(1), pp.51~59 (1996)[DOI]
- S. Murala, "Color and texture features for image indexing and retrieval", Proceedings of the IEEE International Advance Computing Conference, pp.1411~1416 (2009)
- S. M. Singh, "Content-based image retrieval using color moment and Gabor texture feature", International Journal of Computer Science Issues, 9(5), pp.299~309 (2012)
- M. Singha, "Content based image retrieval using color and texture", Signal & Image Processing, 3(1), pp.39~57 (2012)
- X. Y. Wang, "An effective image retrieval scheme using color, texture and shape features", Computer Standards & Interfaces, 33(1), pp.59~68 (2011)[DOI]
- M. V. Lande, "An effective content-based image retrieval using color, texture and shape feature", Intelligent Computing, Networking, and Informatics, pp.1163~1170 (2014)
- J. Wan, "Deep learning for content-based image retrieval: a comprehensive study", Proceedings of the 22nd ACM International Conference on Multimedia, pp.157~166 (2014)
- Q. Xu, "Feature fusion based image retrieval using deep learning", Journal of Information & Computational Science, 12(6), pp.2361~2373 (2015)[DOI]
- D. G. Lowe, "Object recognition from local scale-invariant features", Proceedings of the 7th IEEE International Conference on Computer Vision, pp.1150~1157 (1999)
- K. Mikolajczyk, "A performance evaluation of local descriptors", IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10), pp.1615~1630 (2005)[DOI]
- S. A. J. Winder, "Learning local image descriptors", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1~8 (2007)
- E. Tola, "DAISY: an efficient dense descriptor applied to wide-baseline stereo", IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(6), pp.815~830 (2010)[DOI]
- S. Bougueroua, "Ellipse based local binary pattern for color image retrieval", Proceedings of the 4th International Symposium on ISKO-Maghreb: Concepts and Tools for Knowledge Management, pp.1~8 (2014)
- S. Murala, "Directional local extrema patterns: a new descriptor for content based image retrieval", International Journal of Multimedia Information Retrieval, 1(3), pp.191~203 (2012)[DOI]
- R. M. Haralick, "Textural features for image classification", IEEE Transactions on Systems, Man, and Cybernetics, 3(6), pp.610~621 (1973)
- F. Zhou, "Texture feature based on local Fourier transform", Proceedings of the International Conference on Image Processing, pp.610~613 (2001)
- H. B. Kekre, "CBIR using upper six FFT sectors of color images for feature vector generation", International Journal of Engineering and Technology, 2(2), pp.49~54 (2010)
- F. Malik, "The statistical quantized histogram texture features analysis for image retrieval based on median and Laplacian filters in the DCT domain", The International Arab Journal of Information Technology, 10(6), pp.1~9 (2013)
- H. B. Kekre, "Content based image retrieval using full Haar sectorization", International Journal of Image Processing, 5(1), pp.1~12 (2011)[DOI]
- J. Z. Wang, "Content-based image indexing and searching using Daubechies' wavelets", International Journal on Digital Libraries, 1(4), pp.311~328 (1998)[DOI]
- B. S. Manjunath, "Texture features for browsing and retrieval of image data", IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(8), pp.837~842 (1996)[DOI]
- T. Ojala, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns", IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), pp.971~987 (2002)[DOI]
- H. Jin, "Face detection using improved LBP under Bayesian framework", Proceedings of the 3rd International Conference on Image and Graphics, pp.306~309 (2004)
- X. Tan, "Enhanced local texture feature sets for face recognition under difficult lighting conditions", Analysis and Modeling of Faces and Gestures, pp.168~182 (2007)
- B. Zhang, "Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor", IEEE Transactions on Image Processing, 19(2), pp.533~544 (2010)[DOI]
- P. V. N. Reddy, "Content based image retrieval using local derivative patterns", Journal of Theoretical and Applied Information Technology, 28(2), pp.95~103 (2011)
- Y. Xia, "Local spatial binary pattern: a new feature descriptor for content-based image retrieval", Proceedings of the 5th International Conference on Graphic and Image Processing (2013)
- V. Takala, "Block-based methods for image retrieval using local binary patterns", Image Analysis, pp.882~891 (2005)
- "Corel 1k (Wang)"[URL]
- J. Z. Wang, "SIMPLIcity: semantics-sensitive integrated matching for picture libraries", IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9), pp.947~963 (2001)[DOI]