Enter feature in image smoothly

Aug 6th, 2022
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How to Enter feature in image

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hello friends in this lecture we will discuss about the feature extraction from two-dimensional color images so let us start before starting let me clear one thing that the features of an image can be extracted by its content content like color texture shape position dominant edges of image items and regions etcetera now in this video we will use color content of an image to extract the features of that image right let us take an example here we have an image of sunset and we all know that an image can be represented in the form of pixels so this is the representation of the sunset image in the form of pixels next step is to draw the histogram of this image I am assuming that there are only 256 colors which are used in this image I mean the color of any pixel in this image lies in the range of these 256 colors in this Instagram we have colors on the x-axis and the pixel count percentage on y-axis I have taken some dummy values to explain this point for example 75% of the pixels in thi

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Feature extraction is a part of the dimensionality reduction process, in which, an initial set of the raw data is divided and reduced to more manageable groups. So when you want to process it will be easier. The most important characteristic of these large data sets is that they have a large number of variables.
Low-level features include edges and blobs, and high-level features include objects and events. Loosely, the low-level feature extraction is based on signal/image processing techniques, while the high-level feature extraction is based on machine learning techniques.
What Are Local Features? Local features refer to a pattern or distinct structure found in an image, such as a point, edge, or small image patch. They are usually associated with an image patch that differs from its immediate surroundings by texture, color, or intensity.
Features are parts or patterns of an object in an image that help to identify it. For example a square has 4 corners and 4 edges, they can be called features of the square, and they help us humans identify its a square. Features include properties like corners, edges, regions of interest points, ridges, etc.
Contouring, image sharpening, blurring, embossing and edge detection are typical image processing functions (see Table 4.1). Low and high pass filters (spatial filters) are used when the filtering is based on pixel values and gradients to smooth and reduce noise and details.
The technique which helps in reducing a large set of features by selecting effective features and discarding the redundant features from the original dataset is called Feature selection (FS)/Feature subset selection (FSS) (Blum Langley, 1997. (1997).
Color Gradient Histogram This method simply measures the proportions of red, green, and blue values of an image and finds an image with similar color proportions. Color gradient histograms can be tuned primarily through binning the values.
Feature extraction refers to the process of transforming raw data into numerical features that can be processed while preserving the information in the original data set. It yields better results than applying machine learning directly to the raw data.

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