Pedestrian with Direction Detection Using the Combination of Decision Tree Learning and SVM

Santoshi, G.; Mishra, Sarita

doi:10.1007/978-3-319-13728-5_28

Cited by 6 publications

(1 citation statement)

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“…The classification can be categorized into two types, supervised learning and unsupervised learning. Supervised learning method should have a input and target output using which the data is trained .The algorithms proposed under supervised classifications [1] are probabilistic models, linear regression, Support vector machine [4], logistic regression etc. Unsupervised learning method only the input data is present using which the output data has to be obtained.…”

Section: Introductionmentioning

confidence: 99%

Depth Estimation using SVM for Image

Santoshi¹

2018

IJRASET

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The depth of an image is used to find out the distance from where it is taken. This is done by taking a set of images with different depths as the input and extracting Local and Global features from it. First, feature extraction based methods are utilized to evaluate image similarities. We propose a model that incorporates both of them to obtain significantly more accurate depth estimates than using either global or local properties alone. The features thus obtained are tested using support vector machines. The trained data is then used for testing to calculate the depth of any image approximately.

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Section: Introductionmentioning

confidence: 99%