09-07-2014, 10:53 AM
Feature Extraction and Analysis
Feature Extraction and Analysis.ppt (Size: 746.5 KB / Downloads: 459)
Introduction
The goal in image analysis is to extract useful information for solving application-based problems.
The first step to this is to reduce the amount of image data using methods that we have discussed before:
Image segmentation
Filtering in frequency domain
The next step would be to extract features that are useful in solving computer imaging problems.
What features to be extracted are application dependent.
After the features have been extracted, then analysis can be done
Binary Object Features
In order to extract object features, we need an image that has undergone image segmentation and any necessary morphological filtering.
This will provide us with a clearly defined object which can be labeled and processed independently.
After all the binary objects in the image are labeled, we can treat each object as a binary image.
The labeled object has a value of ‘1’ and everything else is ‘0’.
The labeling process goes as follows:
Define the desired connectivity.
Scan the image and label connected objects with the same symbol.
After we have labeled the objects, we have an image filled with object numbers.
This image is used to extract the features of interest.
Among the binary object features include area, center of area, axis of least second moment, perimeter, Euler number, projections, thinness ration and aspect ratio.
Histogram Features
The histogram of an image is a plot of the gray-level values versus the number of pixels at that value.
The shape of the histogram provides us with information about the nature of the image.
The characteristics of the histogram has close relationship with characteristic of image such as brightness and contrast.
Histogram Features – Skew
The skew measures the asymmetry (unbalance) about the mean in the gray-level distribution.
Image with bimodal histogram distribution (object in contrast background) should have high standard deviation but low skew distribution (one peak at each side of mean).
Color Features
However, using absolute color measure such as RGB color space is not robust.
There are many factors that contribute to color: lighting, sensors, optical filtering, and any print or photographic process.
Any change in these factors will change the absolute color measure.
Any system developed based on absolute color measure will not work when any of these factors change
Spectral Images
The ring measure can be used to find texture:
High power in small radii corresponds to smooth textures.
High power in large radii corresponds to coarse texture.
The sector power measure can be used to find lines or edges in a given direction, but the results are size invariant.
Feature Analysis
Important to aid in feature selection process
Initially, features selected based on understanding of the problem and developer’s experience
FA then will examine carefully to see the most useful & put back through feedback loop
To define the mathematical tools – feature vectors, feature spaces, distance & similarity measurement
Feature Vectors
A feature vector is a method to represent an image or part of an image.
A feature vector is an n-dimensional vector that contains a set of values where each value represents a certain feature.
This vector can be used to classify an object, or provide us with condensed higher-level information regarding the image.
Distance & Similarity Measures
Feature vector is to present the object and will be used to classify it
To perform classification, need to compare two feature vectors
2 primary methods – difference between two or similarity
Two vectors that are closely related will have small difference and large similarity
Similarity Measures
The second type of metric used for comparing two feature vectors is the similarity measure.
The most common form of the similarity measure is the vector inner product.
Using our definition of vector A and B, the vector inner product can be defined by the following equation:
Similarity Measures
When selecting a feature for use in a computer imaging application, an important factor is the robustness of the feature.
A feature is robust if it will provide consistent results across the entire application domain.
For example, if we develop a system to work under any lightning conditions, we do not want to use features that are lightning dependent
Conclusion
Feature Extraction
Binary Object Features (Area, Center of Area, Axis of Least Second Moment, Perimeter, Thinness Ratio, Irregularity, Aspect Ratio, Euler Number, Projection)
Histogram Features (Mean, Standard Deviation, Skew, Energy, Entropy)
Color Features
Spectral Features
Feature Analysis
Feature Vectors and Feature Spaces
Distance and Similarity Measures (Euclidean distance, Range-normalized Euclidean distance, City block or absolute value metric, Maximum value)