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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Published on: August 30, 2013

Revisiting linear discriminant techniques in gender recognition.

Juan Bekios-Calfa¹, José M Buenaposada, Luis Baumela

¹Departamento de Ingeniería de Sistemas y Computación, Universidad Católica del Norte, Chile. juan.bekios@ucn.cl

IEEE Transactions on Pattern Analysis and Machine Intelligence

|December 8, 2010

Summary

This summary is machine-generated.

Linear classification methods offer efficient gender classification for resource-limited computing. Support Vector Machines (SVMs) excel with ample data and resources, while linear and boosting methods suit scenarios with limited resources.

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Area of Science:

Computer Vision
Pattern Recognition
Machine Learning

Background:

Resource-limited algorithms are crucial for emerging computer vision applications on mobile and networked devices.
Linear classification techniques offer simplicity and low computational demands, making them suitable for such environments.

Purpose of the Study:

To review the state-of-the-art in gender classification, focusing on linear techniques.
To identify limitations of linear techniques and demonstrate methods for achieving competitive performance.
To evaluate the impact of data and computational resources on classifier performance.

Main Methods:

Comparative analysis of Support Vector Machines (SVMs), boosting algorithms, and Linear Discriminant Analysis (LDA).
Single-database and cross-database experiments were conducted to assess classification accuracies.
Feature selection was employed for the Linear Discriminant Analysis approach.

Main Results:

Single-database experiments showed comparable accuracies for SVMs, boosting, and LDA.
Cross-database experiments revealed optimistic bias in single-database results.
SVMs demonstrated superior performance when sufficient data and computational resources were available.

Conclusions:

The choice of gender classification technique depends on the availability of training data and computational resources.
For scarce resources, linear or boosting approaches are adequate.
When both data and resources are very limited, linear methods are the optimal choice for gender classification.