Related Experiment Video
Updated: Aug 5, 2026
12:08
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
Published on: August 13, 2014
Vectorial Image Representation on the Texture Space (VIR-TS) Applied to RGB Image Classification: Face Recognition
Héctor Guillen-Bonilla1, José Trinidad Guillen-Bonilla2, Maricela Jiménez-Rodríguez3
1Departamento de Ingeniería de Proyectos, Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Blvd. M. García Barragán 1421, Guadalajara 44410, Jalisco, Mexico.
Journal of Imaging
|July 27, 2026
Summary
The Vectorial Image Representation on the Texture Space (VIR-TS) transform efficiently classifies RGB images, achieving 100% accuracy with high signal-to-noise ratios. Its effectiveness decreases with increased class noise.
Area of Science:
- Computer Vision
- Image Processing
- Pattern Recognition
Background:
- Accurate image classification is crucial for various applications.
- Existing methods may struggle with noisy data or require complex feature extraction.
Purpose of the Study:
- To evaluate the classification efficiency of the Vectorial Image Representation on the Texture Space (VIR-TS) transform for RGB images.
- To determine the impact of noise and signal-to-noise ratio on VIR-TS performance.
Main Methods:
- RGB images were decomposed into individual color channels (SR, SG, SB).
- The VIR-TS transform was applied to each channel, generating channel-specific vectors (C→R, C→G, C→B).
- A multi-class classifier utilized these vectors to identify images within a database.
Main Results:
- VIR-TS achieved 100% classification efficiency when no noise was present or signal-to-noise ratio was high.
- Efficiency decreased as class noise increased, ranging from 95% down to 10%.
Conclusions:
- The VIR-TS transform demonstrates high efficiency in RGB image classification, particularly under optimal conditions.
- Its potential applications include security systems, access control, medical diagnosis, and drone-based computer vision.
Related Concept Videos
01:19Gradient Vectors and Their Applications
Every point on a topographical map corresponds to a particular elevation, so the landscape can be modeled as a surface whose height depends on horizontal position. From any given location, a hiker may face infinitely many directions, but only one direction produces the fastest possible increase in elevation. This unique route is called the direction of steepest ascent, and in multivariable calculus, it is represented by the gradient vector of the elevation function.The gradient vector points...
01:17Vector or Cross Product
Vector multiplication of two vectors yields a vector product, with the magnitude equal to the product of the individual vectors multiplied by the sine of the angle between both the vectors and the direction perpendicular to both the individual vectors. As there are always two directions perpendicular to a given plane, one on each side, the direction of the vector product is governed by the right-hand thumb rule.
Consider the cross product of two vectors. Imagine rotating the first vector about...
Consider the cross product of two vectors. Imagine rotating the first vector about...
01:16Vector Representation of Complex Numbers
Complex numbers, represented in Cartesian coordinates, can also be visualized as vectors. These vectors can be expressed in polar form, emphasizing their magnitude and angle. When a complex number is input into a function, the output is another complex number, highlighting the function's zero point from which the vector representation can originate.
Consider a function defined as the product of the complex factors in the numerator divided by the product of the complex factors in the denominator.
Consider a function defined as the product of the complex factors in the numerator divided by the product of the complex factors in the denominator.
01:29Vector Components in the Cartesian Coordinate System
Vectors are usually described in terms of their components in a coordinate system. Even in everyday life, we naturally invoke the concept of orthogonal projections in a rectangular coordinate system. For example, if someone gives you directions for a particular location, you will be told to go a few km in a direction like east, west, north, or south, along with the angle in which you are supposed to move. In a rectangular (Cartesian) xy-coordinate system in a plane, a point in a plane is...
01:08Vector Algebra: Method of Components
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
In many applications, the magnitudes and directions of...
01:10Vector Algebra: Graphical Method
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...