# Information-theoretic Dictionary Learning for Image Classification

Source: [Information-theoretic Dictionary Learning for Image Classification](<https://www.sota2.com/research/paper/68fa51a41e209131ce975738>)

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

Data updated: 2025-10-27T04:57:47.618Z

Authors: Qiang Qiu, Vishal M. Patel, Rama Chellappa

Published: 2012-08-17T20:38:56.000Z

## Abstract

We present a two-stage approach for learning dictionaries for object classification tasks based on the principle of information maximization. The proposed method seeks a dictionary that is compact, discriminative, and generative. In the first stage, dictionary atoms are selected from an initial dictionary by maximizing the mutual information measure on dictionary compactness, discrimination and reconstruction. In the second stage, the selected dictionary atoms are updated for improved reconstructive and discriminative power using a simple gradient ascent algorithm on mutual information. Experiments using real datasets demonstrate the effectiveness of our approach for image classification tasks.

## 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/1208.3687>)

[PDF](<https://arxiv.org/pdf/1208.3687.pdf>)