Neural Method For Explicit Mapping Of Quasi-curvature Locally Linear Embedding In Image Retrieval
Shenglan Liu, Jun Wu, Lin Feng, Feilong Wang . Arxiv 2017 – 0 citations
[Paper]
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.