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Quantization Selection of Colour Histogram Bins to Categorize the Colour Appearance of Landscape Paintings for Image Retrieval

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@article{IJASEIT1381,
   author = {Aniza Othman and Tengku Siti Meriam Tengku Wook and Shereena M. Arif},
   title = {Quantization Selection of Colour Histogram Bins to Categorize the Colour Appearance of Landscape Paintings for Image Retrieval},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {6},
   number = {6},
   year = {2016},
   pages = {930--936},
   keywords = {Colour Concept; Colour Appearance Feature Vector;Image Classification; CIELab Colour Model},
   abstract = {

In the world of today, most images are digitized and kept in digital libraries for better organization and management. With the growth of information and communication technology, collection holders such as museums or cultural institutions have been increasingly interested in making their collections available anytime and anywhere for any Image Retrieval (IR) activities such as browsing and searching. In a colour image retrieval application, images retrieved by users are accomplished according to their specifications on what they want or acquire, which could be based upon so many concepts. We suggest an  approach to categorize the colour appearances of whole scene landscape painting images based on human colour perception. The colour features in the image are represented using a colour histogram. We then find  the suitable quantization bins that can be used to generate optimum colour histograms for all categories of colour appearances, which is selected based on theHarmonic Mean of the precision and recall,  also known as F-Score percentage higher saturated value. Colour appearance attributes in the CIELab colour model (L-Lightness, a and b are colour-opponent dimension) are used to generate colour appearance feature vectors namely the saturation metric, lightness metric and multicoloured metric. For the categorizations, we use the Nearest Neighbour (NN) method to detect the classes by using the predefined colour appearance descriptor measures and the pre-set thresholds.  The experimental results show that the quantization of CIELab colour model into 11 uniformly bins for each component had achieved the optimum result for all colour appearances categories.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1381},    doi = {10.18517/ijaseit.6.6.1381} }

EndNote

%A Othman, Aniza
%A Tengku Wook, Tengku Siti Meriam
%A M. Arif, Shereena
%D 2016
%T Quantization Selection of Colour Histogram Bins to Categorize the Colour Appearance of Landscape Paintings for Image Retrieval
%B 2016
%9 Colour Concept; Colour Appearance Feature Vector;Image Classification; CIELab Colour Model
%! Quantization Selection of Colour Histogram Bins to Categorize the Colour Appearance of Landscape Paintings for Image Retrieval
%K Colour Concept; Colour Appearance Feature Vector;Image Classification; CIELab Colour Model
%X 

In the world of today, most images are digitized and kept in digital libraries for better organization and management. With the growth of information and communication technology, collection holders such as museums or cultural institutions have been increasingly interested in making their collections available anytime and anywhere for any Image Retrieval (IR) activities such as browsing and searching. In a colour image retrieval application, images retrieved by users are accomplished according to their specifications on what they want or acquire, which could be based upon so many concepts. We suggest an  approach to categorize the colour appearances of whole scene landscape painting images based on human colour perception. The colour features in the image are represented using a colour histogram. We then find  the suitable quantization bins that can be used to generate optimum colour histograms for all categories of colour appearances, which is selected based on theHarmonic Mean of the precision and recall,  also known as F-Score percentage higher saturated value. Colour appearance attributes in the CIELab colour model (L-Lightness, a and b are colour-opponent dimension) are used to generate colour appearance feature vectors namely the saturation metric, lightness metric and multicoloured metric. For the categorizations, we use the Nearest Neighbour (NN) method to detect the classes by using the predefined colour appearance descriptor measures and the pre-set thresholds.  The experimental results show that the quantization of CIELab colour model into 11 uniformly bins for each component had achieved the optimum result for all colour appearances categories.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1381 %R doi:10.18517/ijaseit.6.6.1381 %J International Journal on Advanced Science, Engineering and Information Technology %V 6 %N 6 %@ 2088-5334

IEEE

Aniza Othman,Tengku Siti Meriam Tengku Wook and Shereena M. Arif,"Quantization Selection of Colour Histogram Bins to Categorize the Colour Appearance of Landscape Paintings for Image Retrieval," International Journal on Advanced Science, Engineering and Information Technology, vol. 6, no. 6, pp. 930-936, 2016. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.6.6.1381.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Othman, Aniza
AU  - Tengku Wook, Tengku Siti Meriam
AU  - M. Arif, Shereena
PY  - 2016
TI  - Quantization Selection of Colour Histogram Bins to Categorize the Colour Appearance of Landscape Paintings for Image Retrieval
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 6 (2016) No. 6
Y2  - 2016
SP  - 930
EP  - 936
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Colour Concept; Colour Appearance Feature Vector;Image Classification; CIELab Colour Model
N2  - 

In the world of today, most images are digitized and kept in digital libraries for better organization and management. With the growth of information and communication technology, collection holders such as museums or cultural institutions have been increasingly interested in making their collections available anytime and anywhere for any Image Retrieval (IR) activities such as browsing and searching. In a colour image retrieval application, images retrieved by users are accomplished according to their specifications on what they want or acquire, which could be based upon so many concepts. We suggest an  approach to categorize the colour appearances of whole scene landscape painting images based on human colour perception. The colour features in the image are represented using a colour histogram. We then find  the suitable quantization bins that can be used to generate optimum colour histograms for all categories of colour appearances, which is selected based on theHarmonic Mean of the precision and recall,  also known as F-Score percentage higher saturated value. Colour appearance attributes in the CIELab colour model (L-Lightness, a and b are colour-opponent dimension) are used to generate colour appearance feature vectors namely the saturation metric, lightness metric and multicoloured metric. For the categorizations, we use the Nearest Neighbour (NN) method to detect the classes by using the predefined colour appearance descriptor measures and the pre-set thresholds.  The experimental results show that the quantization of CIELab colour model into 11 uniformly bins for each component had achieved the optimum result for all colour appearances categories.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1381 DO - 10.18517/ijaseit.6.6.1381

RefWorks

RT Journal Article
ID 1381
A1 Othman, Aniza
A1 Tengku Wook, Tengku Siti Meriam
A1 M. Arif, Shereena
T1 Quantization Selection of Colour Histogram Bins to Categorize the Colour Appearance of Landscape Paintings for Image Retrieval
JF International Journal on Advanced Science, Engineering and Information Technology
VO 6
IS 6
YR 2016
SP 930
OP 936
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Colour Concept; Colour Appearance Feature Vector;Image Classification; CIELab Colour Model
AB 

In the world of today, most images are digitized and kept in digital libraries for better organization and management. With the growth of information and communication technology, collection holders such as museums or cultural institutions have been increasingly interested in making their collections available anytime and anywhere for any Image Retrieval (IR) activities such as browsing and searching. In a colour image retrieval application, images retrieved by users are accomplished according to their specifications on what they want or acquire, which could be based upon so many concepts. We suggest an  approach to categorize the colour appearances of whole scene landscape painting images based on human colour perception. The colour features in the image are represented using a colour histogram. We then find  the suitable quantization bins that can be used to generate optimum colour histograms for all categories of colour appearances, which is selected based on theHarmonic Mean of the precision and recall,  also known as F-Score percentage higher saturated value. Colour appearance attributes in the CIELab colour model (L-Lightness, a and b are colour-opponent dimension) are used to generate colour appearance feature vectors namely the saturation metric, lightness metric and multicoloured metric. For the categorizations, we use the Nearest Neighbour (NN) method to detect the classes by using the predefined colour appearance descriptor measures and the pre-set thresholds.  The experimental results show that the quantization of CIELab colour model into 11 uniformly bins for each component had achieved the optimum result for all colour appearances categories.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1381 DO - 10.18517/ijaseit.6.6.1381