International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 8, No. 3, 8 Mar. 2016
Cover page and Table of Contents: PDF (size: 892KB)
Novel Directional Local Difference Binary Patterns (DLDBP) for Image, and Video Indexing and Retrieval
Full Text (PDF, 892KB), PP.55-62
Views: 0 Downloads: 0
Author(s)
M.Ravinder
1,*
T.Venugopal
2
Index Terms
Image, Indexing, Retrieval, Directional local difference binary patterns (DLDBP), LBP, CS-LBP, video, key-frame
Abstract
In this paper, we propose a novel algorithm based on directional local difference binary patterns useful for content based image indexing and retrieval. The popular and successful method local binary patterns (LBP) codify a pixel, based on the neighborhood gray values around the pixel. Another flavor of LBP is, center symmetric local binary patterns (CS-LBP), which is the base method for our proposed novel algorithm. The proposed method is based on the directional difference between neighboring pixels. The four directional local difference binary patterns (DLDBP) in 0o, 45o, 90o, and 135o directions are proposed. Then, we apply our method on benchmark image database Corel-1k. The proposed DLDBP (Directional Local Difference Binary Patterns) can also be used to represent a video, using a key frame in the video. We apply the proposed directional local difference binary patterns (DLDBP) key frame based algorithm, on a video database, which consists of ten videos of airplane, ten videos of sailing boat , ten videos of car, and ten videos are of war tank. The performance of proposed DLDBP (Directional Local Difference Binary Patterns) is compared with CS-LBP (Central Symmetric Local Binary Patterns) method. The performance of DLDBP key frame based method is compared with volume local binary patterns (VLBP) method.
Cite This Paper
M.Ravinder, T.Venugopal,"Novel Directional Local Difference Binary Patterns (DLDBP) for Image, and Video Indexing and Retrieval", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.3, pp.55-62, 2016. DOI: 10.5815/ijigsp.2016.03.07
Reference
[1]Rui Y, Huang TS "Image retrieval: current techniques, promising directions and open issues". J Vis Commun Image Represent 10:39–62,1999.
[2]Smeulders AWM, Worring M, Santini S, Gupta A, Jain R "Content-based image retrieval at the end of the early years". IEEE Trans Pattern Anal Mach Intell 22(12):1349–1380, 2000.
[3]Kokare M, Chatterji BN, Biswas PK "A survey on current content based image retrieval methods". IETE J Res 48(3&4):261–271, 2002.
[4]Liu Y, Zhang D, Lu G, Ma W-Y "A survey of contentbased image retrieval with high-level semantics". J Pattern Recognit 40:262–282, 2007.
[5]Ahmadian A, Mostafa A "An efficient texture classification algorithm using Gabor wavelet". In: 25th annual international conference of the IEEE EMBS, pp 930–933, Cancun, Mexico, 2003.
[6]Do MN, Vetterli M "Wavelet-based texture retrieval using generalized Gaussian density and Kullback-leibler distance". IEEE Trans Image Process 11(2):146–158, 2002.
[7]UnserM "Texture classification bywavelet packet signatures". IEEE Trans Pattern Anal Mach Intell 15(11):1186–1191, 1993.
[8]Manjunath BS, Ma WY "Texture features for browsing and retrieval of image data". IEEE Trans Pattern Anal Mach Intell 18(8):837–842, 1996.
[9]Kokare M, Biswas PK, Chatterji BN "Texture image retrieval using rotated wavelet filters". J Pattern Recognit Lett 28:1240–1249, 2007.
[10]Kokare M, Biswas PK, Chatterji BN "Texture image retrieval using new rotated complex wavelet filters". IEEE Trans Syst Man Cybernet 33(6):1168–1178, 2005.
[11]Kokare M, Biswas PK, Chatterji BN "Rotation-invariant texture image retrieval using rotated complex wavelet filters". IEEE Trans Syst Man Cybernet 36(6):1273–1282, 2006.
[12]Ojala T, Pietikainen M, Harwood D "A comparative study of texture measures with classification based on feature distributions". J Pattern Recognit 29(1):51–59, 1996.
[13]PietikainenM, Ojala T, Scruggs T, BowyerKW, Jin C, Hoffman K, Marques J, Jacsik M,WorekW "Overview of the face recognition using feature distributions". J Pattern Recognit 33(1):43–52, 2000.
[14]Huang X, Li SZ, Wang Y "Shape localization based on statistical method using extended local binary patterns". In: Proc Int Conf Image and Graphics, pp 184–187, 2004.
[15]Li M, Staunton RC "Optimum Gabor filter design and local binary patterns for texture segmentation". J Pattern Recognit 29:664–672, 2008.
[16]Zhang B, Gao Y, Zhao S, Liu J "Local derivative pattern versus local binary pattern: Face recognition with higher-order local pattern descriptor". IEEE Trans Image Process 19(2):533–544, 2010.
[17]Subrahmanyam Murala, R.P.Maheshwari, R.Balasubramanian "Directional local extrema patterns: a new descriptor for content based image retrieval" Int J Multimed Info Retr, 2012.
[18]Ibrahim S. I. Abuhaiba, Ruba A. A. Salamah, "Efficient Global and Region Content Based Image Retrieval", I.J. Image, Graphics and Signal Processing, 2012, 5, 38-46.
[19]Hadis Heidari, Abdolah Chalechale, Alireza Ahmadi Mohammadabadi, "Parallel Implementation of Texture Based Image Retrieval on The GPU", I.J. Image, Graphics and Signal Processing, 2013, 9, 36-42.
[20]K.Prasanthi Jasmine, P.Rajesh Kumar, "Color and Rotated M-Band Dual Tree Complex Wavelet Transform Features for Image Retrieval", I.J. Image, Graphics and Signal Processing, 2014, 9, 1-10.
[21]T.V.Madhusudhana Rao, Dr.S.Pallam Setty, Dr.Y.Srinivas, "An Efficient System for Medical Image Retrieval using Generalized Gamma Distribution", I.J.Image, Graphics and Signal Processing, 2015, 6, 52-58.
[22]Heikkil M, Pietikainen M, Schmid C "Description of interest regions with local binary patterns". Pattern Recognit 42:425–436, 2009.
[23]Guoying Zhao, Matti Pietikäinen, "Dynamic Texture Recognition Using Volume Local Binary Patterns".
[24]https://sites.google.com/site/benchmarkvideodata/dataset