# Fusing image representations for classification using support vector machines

Source: [Fusing image representations for classification using support vector machines](<https://www.sota2.com/research/paper/68fa60a41e209131ce9f07cd>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T19:43:33.656Z

Authors: Can Demirkesen, Hocine Cherifi

Published: 2012-07-16T09:23:06.000Z

## Abstract

In order to improve classification accuracy different image representations are usually combined. This can be done by using two different fusing schemes. In feature level fusion schemes, image representations are combined before the classification process. In classifier fusion, the decisions taken separately based on individual representations are fused to make a decision. In this paper the main methods derived for both strategies are evaluated. Our experimental results show that classifier fusion performs better. Specifically Bayes belief integration is the best performing strategy for image classification task.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1207.3607>)

[PDF](<https://arxiv.org/pdf/1207.3607.pdf>)