Local Mesh Co-Occurrence Pattern for Content Based Image Retrieval
This paper presents the local mesh co-occurrence
patterns (LMCoP) using HSV color space for image retrieval system.
HSV color space is used in this method to utilize color, intensity and
brightness of images. Local mesh patterns are applied to define the
local information of image and gray level co-occurrence is used to
obtain the co-occurrence of LMeP pixels. Local mesh co-occurrence
pattern extracts the local directional information from local mesh
pattern and converts it into a well-mannered feature vector using gray
level co-occurrence matrix. The proposed method is tested on three
different databases called MIT VisTex, Corel, and STex. Also, this
algorithm is compared with existing methods, and results in terms of
precision and recall are shown in this paper.
[1] Christoph Palm, “Color texture classification by integrative Cooccurrence
matrices,” Pattern Recognition, vol.37 pp. 965-976(2004)
[2] Fernando Roberti de Siqueira, William Robson Schwartz, and Helio
Pedrini, “Multi-Scale Gray Level Co-Occurrence Matrices for Texture
Description,” Neurocomputing, vol.120 pp. 336-345,2013
[3] Chuen-Horng Lin, Rong-Tai Chen, and Yung-Kuan Chan, “A smart
content-based image retrieval system based on color and texture
feature”, Image and Vision Computing, 2009, 27, 658-665.
[4] A. Vadivel, Shamik Sural, and A.K. Majumdar, “An Integrated Color
and Intensity Co-occurrence Matrix”, Pattern Recognition Letters,
vol.28, pp.974-983, 2007.
[5] Santosh Kumar Vipparthi, and Shyam Krishna Nagar, “Multi-joint
histogram based modeling for image indexing and retrieval”, Computers
and Electrical Engineering, vol.8 pp. 163-173, 2014.
[6] Xiaoyang Tan and Bill Triggs, “Enhanced Local Texture Feature Sets
for Face Recognition Under Difficult Lighting Conditions”, In: Analysis
and Modeling of faces and gestures, 168-182, 2007.
[7] Subrahmanyam Murala, Q.M. Jonathan Wu, R. Balasubramanian ; R.P.
Maheshwari, “Joint histogram between color and local extrema patterns
for object tracking”, Proc. SPIE 8663Video Surveillance and
Transportation Imaging Applications, 2013.
[8] Baochang Zhang, Yongsheng Gao, Sanqiang Zhao, Jianzhuang Liu,
Local derivative pattern versus local binary pattern: Face recognition
with higher-order local pattern descriptor, IEEE Transactions on image
processing, vol.19 pp.533-544, 2010.
[9] Subrahmanyam Murala, Q.M. Jonathan Wu, Local ternary cooccurrence
patterns: A new feature descriptor for MRI and CT image
retrieval, Neurocomputing, vol.119 pp.399-412,2013.
[10] Subrahmanyam Murala, R. P. Maheshwari, R. Balasubramanian , Local
maximum edge binary patterns: A new descriptor for image retrieval and
object tracking, Signal Processing, vol.92 pp.1467-1479,2012. [11] K.P.Jasmine and P.Rajesh Kumar, Integration of HSV Color
Histogram and LMEBP Joint Histogram for Multimedia Image
Retrieval, Advances in Intelligent Systems and Computing, vol.243 pp.
753-762, 2014.
[12] Subrahmanyam Murala, R.P. Maheshwari, R. Balasubramanian , Local
Tetra Patterns: A New Feature Descriptor for Content-Based Image
Retrieval, IEEE Transactions on Image Processing, vol. 21 pp. 2874-
2886, 2012.
[13] Cheng-Hao Yao, Shu-Yuan Chen, “Retrieval of translated, rotated and
scaled color textures”, Pattern Recongition, vol.36 pp.913-929, 2003.
[14] Marko Heikkil¨, Matti Pietik¨ainen, Cordelia Schmid, Description of
Interest Regions with Local Binary Patterns, in Computer Vision,
Graphics and Image Processing, pp.58-69, 2006.
[15] Subrahmanyam Murala , Q.M. Jonathan Wu; R. Balasubramanian; R.P.
Maheshwari, “Joint histogram between color and local extrema patterns
for object tracking”, Proc. SPIE Video Surveillance and Transportation
Imaging Applications, pp.86630T, 2013.
[16] Subramaniam Murala, Q. M. Jonathan Wu, “Local Mesh Patterns Versus
Local Binary Patterns: Biomedical image indexing and retrieval” IEEE
J. Bio. Health. Trans, vol.18 pp.2014, 929-938.
[1] Christoph Palm, “Color texture classification by integrative Cooccurrence
matrices,” Pattern Recognition, vol.37 pp. 965-976(2004)
[2] Fernando Roberti de Siqueira, William Robson Schwartz, and Helio
Pedrini, “Multi-Scale Gray Level Co-Occurrence Matrices for Texture
Description,” Neurocomputing, vol.120 pp. 336-345,2013
[3] Chuen-Horng Lin, Rong-Tai Chen, and Yung-Kuan Chan, “A smart
content-based image retrieval system based on color and texture
feature”, Image and Vision Computing, 2009, 27, 658-665.
[4] A. Vadivel, Shamik Sural, and A.K. Majumdar, “An Integrated Color
and Intensity Co-occurrence Matrix”, Pattern Recognition Letters,
vol.28, pp.974-983, 2007.
[5] Santosh Kumar Vipparthi, and Shyam Krishna Nagar, “Multi-joint
histogram based modeling for image indexing and retrieval”, Computers
and Electrical Engineering, vol.8 pp. 163-173, 2014.
[6] Xiaoyang Tan and Bill Triggs, “Enhanced Local Texture Feature Sets
for Face Recognition Under Difficult Lighting Conditions”, In: Analysis
and Modeling of faces and gestures, 168-182, 2007.
[7] Subrahmanyam Murala, Q.M. Jonathan Wu, R. Balasubramanian ; R.P.
Maheshwari, “Joint histogram between color and local extrema patterns
for object tracking”, Proc. SPIE 8663Video Surveillance and
Transportation Imaging Applications, 2013.
[8] Baochang Zhang, Yongsheng Gao, Sanqiang Zhao, Jianzhuang Liu,
Local derivative pattern versus local binary pattern: Face recognition
with higher-order local pattern descriptor, IEEE Transactions on image
processing, vol.19 pp.533-544, 2010.
[9] Subrahmanyam Murala, Q.M. Jonathan Wu, Local ternary cooccurrence
patterns: A new feature descriptor for MRI and CT image
retrieval, Neurocomputing, vol.119 pp.399-412,2013.
[10] Subrahmanyam Murala, R. P. Maheshwari, R. Balasubramanian , Local
maximum edge binary patterns: A new descriptor for image retrieval and
object tracking, Signal Processing, vol.92 pp.1467-1479,2012. [11] K.P.Jasmine and P.Rajesh Kumar, Integration of HSV Color
Histogram and LMEBP Joint Histogram for Multimedia Image
Retrieval, Advances in Intelligent Systems and Computing, vol.243 pp.
753-762, 2014.
[12] Subrahmanyam Murala, R.P. Maheshwari, R. Balasubramanian , Local
Tetra Patterns: A New Feature Descriptor for Content-Based Image
Retrieval, IEEE Transactions on Image Processing, vol. 21 pp. 2874-
2886, 2012.
[13] Cheng-Hao Yao, Shu-Yuan Chen, “Retrieval of translated, rotated and
scaled color textures”, Pattern Recongition, vol.36 pp.913-929, 2003.
[14] Marko Heikkil¨, Matti Pietik¨ainen, Cordelia Schmid, Description of
Interest Regions with Local Binary Patterns, in Computer Vision,
Graphics and Image Processing, pp.58-69, 2006.
[15] Subrahmanyam Murala , Q.M. Jonathan Wu; R. Balasubramanian; R.P.
Maheshwari, “Joint histogram between color and local extrema patterns
for object tracking”, Proc. SPIE Video Surveillance and Transportation
Imaging Applications, pp.86630T, 2013.
[16] Subramaniam Murala, Q. M. Jonathan Wu, “Local Mesh Patterns Versus
Local Binary Patterns: Biomedical image indexing and retrieval” IEEE
J. Bio. Health. Trans, vol.18 pp.2014, 929-938.
@article{"International Journal of Information, Control and Computer Sciences:70635", author = "C. Yesubai Rubavathi and R. Ravi", title = "Local Mesh Co-Occurrence Pattern for Content Based Image Retrieval", abstract = "This paper presents the local mesh co-occurrence
patterns (LMCoP) using HSV color space for image retrieval system.
HSV color space is used in this method to utilize color, intensity and
brightness of images. Local mesh patterns are applied to define the
local information of image and gray level co-occurrence is used to
obtain the co-occurrence of LMeP pixels. Local mesh co-occurrence
pattern extracts the local directional information from local mesh
pattern and converts it into a well-mannered feature vector using gray
level co-occurrence matrix. The proposed method is tested on three
different databases called MIT VisTex, Corel, and STex. Also, this
algorithm is compared with existing methods, and results in terms of
precision and recall are shown in this paper.", keywords = "Content-based image retrieval system, HSV color
space, gray level co-occurrence matrix, local mesh pattern.", volume = "9", number = "6", pages = "1544-6", }
