12 Feature Extraction Techniques in Computer Vision

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Feature extraction is a fundamental step in computer vision where relevant information is extracted from raw data (such as images) to form a more compact and representative feature space. These features aim to capture important patterns and characteristics of the input data, facilitating subsequent analysis and machine learning tasks.

I. The Importance of Feature Extraction:

1. Dimensionality Reduction:

• Problem: Raw data, especially images, can have a large number of pixels or features, leading to a high-dimensional space.
• Solution: Feature extraction reduces the dimensionality of the data by selecting or creating a subset of relevant features, improving computational efficiency and mitigating the curse of dimensionality.

2. Relevance and Discrimination:

• Problem: Not all features in the raw data are equally informative or relevant for a given task.
• Solution: Feature extraction aims to identify and retain the most discriminative information, enhancing the model’s ability to distinguish between different classes or patterns.

3. Noise Reduction:

• Problem: Raw data may contain…