A novel landmark point selection method for L-ISOMAP

2016 ◽  
Author(s):  
Hao Shi ◽  
Baoqun Yin ◽  
Yizhao Bao ◽  
Yingke Lei
Keyword(s):  
Selection Method ◽  
Point Selection ◽  
Landmark Point

scholarly journals Robust L-Isomap with a Novel Landmark Selection Method

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Hao Shi ◽  
Baoqun Yin ◽  
Yu Kang ◽  
Chao Shao ◽  
Jie Gui
Keyword(s):  
Short Circuit ◽  
Selection Method ◽  
Edge Density ◽  
Nonlinear Method ◽  
Topological Stability ◽  
Point Selection ◽  
Landmark Point ◽  
Computation Efficiency ◽  
Effective Performance ◽  
Neighbourhood Graph

Isomap is a widely used nonlinear method for dimensionality reduction. Landmark-Isomap (L-Isomap) has been proposed to improve the scalability of Isomap. In this paper, we focus on two important issues that were not taken into account in L-Isomap, landmark point selection and topological stability. At first, we present a novel landmark point selection method. It first uses a greedy strategy to select some points as landmark candidates and then removes the candidate points that are neighbours of other candidates. The remaining candidate points are the landmark points. The selection method can promote the computation efficiency without sacrificing accuracy. For the topological stability, we define edge density for each edge in the neighbourhood graph. According to the geometrical characteristic of the short-circuit edges, we provide a method to eliminate the short-circuit edge without breaking the data integrity. The approach that integrates L-Isomap with these two improvements is referred to as Robust L-Isomap (RL-Isomap). The effective performance of RL-Isomap is confirmed through several numerical experiments.

scholarly journals A Grid Feature-Point Selection Method for Large-Scale Street View Image Retrieval Based on Deep Local Features

2020 ◽  
Vol 12 (23) ◽  
pp. 3978
Author(s):  
Tianyou Chu ◽  
Yumin Chen ◽  
Liheng Huang ◽  
Zhiqiang Xu ◽  
Huangyuan Tan
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Local Features ◽  
Selection Method ◽  
Image Features ◽  
Feature Point ◽  
Feature Points ◽  
Point Selection ◽  
Retrieval Task ◽  
Street View

Street view image retrieval aims to estimate the image locations by querying the nearest neighbor images with the same scene from a large-scale reference dataset. Query images usually have no location information and are represented by features to search for similar results. The deep local features (DELF) method shows great performance in the landmark retrieval task, but the method extracts many features so that the feature file is too large to load into memory when training the features index. The memory size is limited, and removing the part of features simply causes a great retrieval precision loss. Therefore, this paper proposes a grid feature-point selection method (GFS) to reduce the number of feature points in each image and minimize the precision loss. Convolutional Neural Networks (CNNs) are constructed to extract dense features, and an attention module is embedded into the network to score features. GFS divides the image into a grid and selects features with local region high scores. Product quantization and an inverted index are used to index the image features to improve retrieval efficiency. The retrieval performance of the method is tested on a large-scale Hong Kong street view dataset, and the results show that the GFS reduces feature points by 32.27–77.09% compared with the raw feature. In addition, GFS has a 5.27–23.59% higher precision than other methods.

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