Performance Analysis of Dense Micro-block Difference and SURF Method for Texture Classification
DOI:
https://doi.org/10.26438/ijcse/v7i6.479482Keywords:
Compressive Sensing, Descriptors, SURF, Texture classificationAbstract
The paper proposes a novel picture portrayal for surface characterization. The ongoing headways in the field of fix based highlights compressive detecting and highlight encoding are joined to plan a hearty picture descriptor. In our methodology, we initially propose the neighbourhood highlights, Dense Micro-square Difference (DMD), which catches the nearby structure from the picture patches at high scales. Rather than the pixel we process the little squares from pictures which catch the miniaturized scale structure from it. DMD can be figured productively utilizing vital pictures. The highlights are then encoded utilizing Fisher Vector strategy to get a picture descriptor which thinks about the higher request measurements. The proposed picture portrayal is joined with straight SVM classifier. The analyses are led on the standard surface datasets (KTH-TIPS-2a, Brodatz, and Curet). On KTH-TIPS-2a dataset the proposed strategy beats the best revealed outcomes by 5.5% and has a practically identical exhibition to the best in class techniques on the different datasets.
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