%0 Journal Article %1 rath2014texture %A Rath, N.P. %A Mishra, Bandana %D 2014 %E Das, Dr. Vinu V %J ACEEE International Journal of Signal and Image Processing %K Self_Organizing scen_image %N 1 %P 8 %T Texture Unit based Monocular Real-world Scene Classification using SOM and KNN Classifier %U http://searchdl.org/public/journals/2014/IJSIP/5/1/1402.pdf %V 5 %X In this paper a method is proposed to discriminate real world scenes in to natural and manmade scenes of similar depth. Global-roughness of a scene image varies as a function of image-depth. Increase in image depth leads to increase in roughness in manmade scenes; on the contrary natural scenes exhibit smooth behavior at higher image depth. This particular arrangement of pixels in scene structure can be well explained by local texture information in a pixel and its neighborhood. Our proposed method analyses local texture information of a scene image using texture unit matrix. For final classification we have used both supervised and unsupervised learning using K-Nearest Neighbor classifier (KNN) and Self Organizing Map (SOM) respectively. This technique is useful for online classification due to very less computational complexity.

@article{rath2014texture, abstract = {In this paper a method is proposed to discriminate real world scenes in to natural and manmade scenes of similar depth. Global-roughness of a scene image varies as a function of image-depth. Increase in image depth leads to increase in roughness in manmade scenes; on the contrary natural scenes exhibit smooth behavior at higher image depth. This particular arrangement of pixels in scene structure can be well explained by local texture information in a pixel and its neighborhood. Our proposed method analyses local texture information of a scene image using texture unit matrix. For final classification we have used both supervised and unsupervised learning using K-Nearest Neighbor classifier (KNN) and Self Organizing Map (SOM) respectively. This technique is useful for online classification due to very less computational complexity.}, added-at = {2014-02-13T10:39:22.000+0100}, author = {Rath, N.P. and Mishra, Bandana}, biburl = {https://www.bibsonomy.org/bibtex/247488350d139c5a2fbbecca4826c161d/ideseditor}, editor = {Das, Dr. Vinu V}, interhash = {bedb9ce1ded5262c4b9fdddbc2291d81}, intrahash = {47488350d139c5a2fbbecca4826c161d}, journal = {ACEEE International Journal of Signal and Image Processing}, keywords = {Self_Organizing scen_image}, month = {January}, number = 1, pages = 8, timestamp = {2014-02-13T10:39:22.000+0100}, title = {Texture Unit based Monocular Real-world Scene Classification using SOM and KNN Classifier}, url = {http://searchdl.org/public/journals/2014/IJSIP/5/1/1402.pdf}, volume = 5, year = 2014 }