Sage-Tips

Is HOG a classifier?

To illustrate, this example shows how to classify numerical digits using HOG (Histogram of Oriented Gradient) features [1] and a multiclass SVM (Support Vector Machine) classifier. This type of classification is often used in many Optical Character Recognition (OCR) applications.

Is HOG a neural network?

Convolutional Neural Network is specially designed for the Computer Vision field. It works better on image data. HOG is a widely used feature extraction technique and along with the Gabor filter is used for feature extraction.

Is HOG a machine learning algorithm?

Object recognition Dalal and Triggs used HOG descriptors as features in a support vector machine (SVM); however, HOG descriptors are not tied to a specific machine learning algorithm.

What is HOG face detection?

HOG is a simple and powerful feature descriptor. It is not only used for face detection but also it is widely used for object detection like cars, pets, and fruits. HOG is robust for object detection because object shape is characterized using the local intensity gradient distribution and edge direction.

Do I need to use PCA for Iris classification?

By the way, you may not even need to use PCA to get good classification results. The iris dataset doesn’t have many dimensions and decision trees will already perform well on the untransformed data. If you want to apply PCA to new data, you must have fit a model first on some training dataset.

Does PCA really improve the result of classification task?

Let’s see if PCA really improves the result of classification task. In order to comprove it, my strategy is to apply a neural network over a dataset and see its initial results. Afterwards, I am going to perform PCA before classification and apply the same neural network over the new dataset and last compare both results.

What is the use of PCA in machine learning?

PCA is very useful for reducing many dimensions into a smaller set of dimensions, as humans can not visualize data on more than 3 dimensions it is usually helpful to reduce multidimensional datasets into 2 or 3 dimensions and graph them in order to get a better understanding of the data.

Does PCA Select some features and discard others?

Often, people end up making a mistake in thinking that PCA selects some features out of the dataset and discards others. The algorithm actually constructs new set of properties based on combination of the old ones.