Object Classification using SVM and KD-Tree
Kalpitha N1, S Murali2

1Kalpitha N, Department of Information Science and Engineering, Jyothy Institute of Technology.
2S Murali, Department of Computer Science and Engineering, Maharaja Institute of Technology.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 30, 2020. | PP: 1717-1731 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7868038620/2020©BEIESP | DOI: 10.35940/ijrte.F7868.038620

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In this proposed work, we presented a system to classify the object. Firstly, the given images are segmented using Region merging Segmentation method. Later the background eliminated images are divided into number of blocks viz., 4, 16, 32. The features like Scale Invariant Feature Transform (SIFT) and Histogram of Gradients (HOG) are extracted from divided blocks of size 4, 16, 32. To measure the strength of proposed method we compare the Classification vs Retrieval using Support Vector Machine and KD Tree. We conducted the experimentation on Caltech 101 data set. To study the effect of accuracy in classification we pick images from database randomly. The Performance revels that the SVM achieves good performance.
Keywords: About Four Key Words or Phrases in Alphabetical Order, Separated By Commas.
Scope of the Article: Classification.