# CSIFT Based Locality-constrained Linear Coding for Image Classification

Source: [CSIFT Based Locality-constrained Linear Coding for Image Classification](<https://www.sota2.com/research/paper/68fa610e1e209131ce9f269a>)

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

Data updated: 2025-10-25T19:51:34.486Z

Authors: Chen Junzhou, Li Qing, Peng Qiang, Kin Hong Wong

Published: 2013-09-28T18:05:12.000Z

## Abstract

In the past decade, SIFT descriptor has been witnessed as one of the most robust local invariant feature descriptors and widely used in various vision tasks. Most traditional image classification systems depend on the luminance-based SIFT descriptors, which only analyze the gray level variations of the images. Misclassification may happen since their color contents are ignored. In this article, we concentrate on improving the performance of existing image classification algorithms by adding color information. To achieve this purpose, different kinds of colored SIFT descriptors are introduced and implemented. Locality-constrained Linear Coding (LLC), a state-of-the-art sparse coding technology, is employed to construct the image classification system for the evaluation. The real experiments are carried out on several benchmarks. With the enhancements of color SIFT, the proposed image classification system obtains approximate 3% improvement of classification accuracy on the Caltech-101 dataset and approximate 4% improvement of classification accuracy on the Caltech-256 dataset.

## 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/1309.7484>)

[PDF](<https://arxiv.org/pdf/1309.7484.pdf>)