Characterizationxofxtextureximages with various orientation,xbrightness and scale changes is a difficult issue in Computer vision. This venture proposes two descriptors and utilizations them together to satisfy such assignment for example Histogram Orient Gradient-Local Binary Pattern(HOG-LBP) include extraction. The proposed framework comprises of pretreatment, highlight extraction and grouping. Initial, a HOG-LBP highlight descriptor is proposed to speak to multi-scale, multi-edge signal data. The HOG segment gives the gesturexedgexgradientxinformation and the LBP gives the texture feature data, which can adjust for the absence of revolution invariance of a solitary element and improve the acknowledgment rate of motions at different scales and numerous edges. At long last, the K-NN classifier is used to understand the image characterization. Trial results on the Brodatz informational collections demonstrate that the proposed strategy can accomplish best accuracy than the other metods. Investigations on the Brodatz database likewise exhibit the execution of the proposed strategy, on the first picture apply create LBP and HOG. Also, log-polar (LP) change is connected on the first picture, and the energies of coefficients on detail sub groups of the log-polar picture these are taken as worldwide texture highlights. We meld the two sorts of highlights for texture order, and the exploratory outcomes on benchmark datasets demonstrate that our proposed technique can accomplish preferable execution over other cutting edge strategies.

orientation, HOG-LBP, K-NN, Brodatz, cutting edge

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