Neural method for Explicit Mapping of Quasi-curvature Locally Linear Embedding in image retrieval
This work addresses efficient image retrieval by providing an explicit dimensionality reduction approach, but it appears incremental as it builds on existing out-of-sample methods.
The paper tackled the problem of explicit nonlinear dimensionality reduction for image retrieval by proposing a Quasi-curvature Locally Linear Embedding (QLLE) combined with a neural method (NM) for out-of-sample extension, achieving better performance than other state-of-the-art methods on three benchmark datasets.
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.