TY - GEN
T1 - Mining visual complexity of images based on an enhanced feature space representation
AU - Iliyasu, Abdullah M.
AU - Al-Asmari, Awad Kh
AU - Abdelwahab, Mohamed A.
AU - Salama, Ahmed S.
AU - Al-Qodah, Mohamed A.
AU - Khan, Asif R.
AU - Le, Phuc Q.
AU - Yan, Fei
PY - 2013
Y1 - 2013
N2 - An enhanced feature space to represent visual complexity of images, as would the HVS, is presented. Specifically, the ratio between the coherent and incoherent pixels in an image was used as a measure of the chromatic contributions to the visual complexity of an image. Similarly, the contrast, energy, entropy and homogeneity were modelled as the textural attributes of an image's visual complexity. Integrated into the SND feature space, these new (chromatic and textural) features facilitate a better and enhanced representation of visual complexity. Using the Corel 1000A dataset to validate the veracity of the proposal, the enhanced visual complexity space, the SND+ space, improves the capability to better represent visual complexity by a 16.7% increase in the exact correlation with a subjective (human) evaluation of the same dataset over the original SND space. Pursued further, the effective representation of visual complexity would have profound impacts in many areas of image processing and computer vision.
AB - An enhanced feature space to represent visual complexity of images, as would the HVS, is presented. Specifically, the ratio between the coherent and incoherent pixels in an image was used as a measure of the chromatic contributions to the visual complexity of an image. Similarly, the contrast, energy, entropy and homogeneity were modelled as the textural attributes of an image's visual complexity. Integrated into the SND feature space, these new (chromatic and textural) features facilitate a better and enhanced representation of visual complexity. Using the Corel 1000A dataset to validate the veracity of the proposal, the enhanced visual complexity space, the SND+ space, improves the capability to better represent visual complexity by a 16.7% increase in the exact correlation with a subjective (human) evaluation of the same dataset over the original SND space. Pursued further, the effective representation of visual complexity would have profound impacts in many areas of image processing and computer vision.
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U2 - 10.1109/WISP.2013.6657484
DO - 10.1109/WISP.2013.6657484
M3 - Conference contribution
AN - SCOPUS:84892660147
SN - 9781467345439
T3 - 2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013 - Proceedings
SP - 65
EP - 70
BT - 2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013 - Proceedings
PB - IEEE Computer Society
T2 - 2013 IEEE 8th International Symposium on Intelligent Signal Processing, WISP 2013
Y2 - 16 September 2013 through 18 September 2013
ER -