# Neural method for Explicit Mapping of Quasi-curvature Locally Linear Embedding in image retrieval

## TL;DR

This paper introduces a neural network-based explicit nonlinear dimensionality reduction method combining Quasi-curvature Locally Linear Embedding with neural mapping, improving image retrieval performance on benchmark datasets.

## Contribution

It presents a novel explicit nonlinear embedding approach that effectively addresses the out-of-sample problem in image retrieval using neural networks.

## Key findings

- Outperforms state-of-the-art out-of-sample methods
- Effective in three benchmark datasets
- Provides efficient image retrieval results

## Abstract

This paper proposed a new explicit nonlinear dimensionality reduction using neural networks for image retrieval tasks. We first proposed a Quasi-curvature Locally Linear Embedding (QLLE) for training set. QLLE guarantees the linear criterion in neighborhood of each sample. Then, a neural method (NM) is proposed for out-of-sample problem. Combining QLLE and NM, we provide a explicit nonlinear dimensionality reduction approach for efficient image retrieval. The experimental results in three benchmark datasets illustrate that our method can get better performance than other state-of-the-art out-of-sample methods.