Salient Feature Selection for CNN-Based Visual Place Recognition

Chen, Yutian; Gan, Wenyan; Jiao, Shanshan; Xu, You-wei; Feng, Yuntian

doi:10.1587/transinf.2018edp7175

Cited by 5 publications

(7 citation statements)

References 21 publications

(33 reference statements)

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“…Experimental results showed that SSM-VPR achieved an impressive recognition effect. However, in terms of image representation space and matching efficiency, SSM-VPR was inferior to the method in [30]. Besides, the inability to distinguish the saliency of landmark areas was also a disadvantage of these methods.…”

Section: Related Workmentioning

confidence: 98%

“…Based on pre-trained CNN models, they fully analyzed the meaning of the original CNN features and made full use of the activations for landmark selection. Chen et al [30] evaluated the feature effectiveness of feature maps obtained from the layer of CNN by variance and proposed a novel method that reserved salient feature maps to achieve fast image matching. This method greatly reduced the space of image representation to half of the original CNN features with a tolerable loss in accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…In other words, the feature vector 𝑓𝑓 𝑖𝑖,𝑗𝑗 may have a very large dimension. According to the visualization result of the feature maps mentioned in [30], a lot of depth feature values are close to zero. The calculation of the number of non-zero values of the vector 𝑓𝑓 𝑖𝑖,𝑗𝑗 may exaggerate the contribution of the close-to-zero values to the significance of the region.…”

Section: Region Selection and Feature Encodingmentioning

confidence: 99%

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Efficient Visual Place Recognition by Adaptive CNN Landmark Matching

2021

KSII TIIS

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Visual place recognition (VPR) is a fundamental yet challenging task of mobile robot navigation and localization. The existing VPR methods are usually based on some pairwise similarity of image descriptors, so they are sensitive to visual appearance change and also computationally expensive. This paper proposes a simple yet effective four-step method that achieves adaptive convolutional neural network (CNN) landmark matching for VPR. First, based on the features extracted from existing CNN models, the regions with higher significance scores are selected as landmarks. Then, according to the coordinate positions of potential landmarks, landmark matching is improved by removing mismatched landmark pairs. Finally, considering the significance scores obtained in the first step, robust image retrieval is performed based on adaptive landmark matching, and it gives more weight to the landmark matching pairs with higher significance scores. To verify the efficiency and robustness of the proposed method, evaluations are conducted on standard benchmark datasets. The experimental results indicate that the proposed method reduces the feature representation space of place images by more than 75% with negligible loss in recognition precision. Also, it achieves a fast matching speed in similarity calculation, satisfying the real-time requirement.

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Section: Related Workmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Section: Region Selection and Feature Encodingmentioning

confidence: 99%

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Efficient Visual Place Recognition by Adaptive CNN Landmark Matching

2021

KSII TIIS

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“…[27][28][29] Among these methods, feature extraction on the entire image for place recognition introduces redundant information and reduces efficiency. Considering that CNN leads to the large space of image representation, Chen et al 30 proposed a new method of reserving salient feature maps and make adaptive binarization on it, and the experimental results proved the effectiveness. Since the highly representative landmark features are robust to appearance changes, Xin et al 31 proposed an effective method based on CNNs and content-based multiscale landmarks to complete the task of place recognition.…”

Section: Related Workmentioning

confidence: 99%

Robust place recognition based on salient landmarks screening and convolutional neural network features

Niu

Qian

2020

International Journal of Advanced Robotic Systems

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In this work, we propose a robust place recognition measurement in natural environments based on salient landmark screening and convolutional neural network (CNN) features. First, the salient objects in the image are segmented as candidate landmarks. Then, a category screening network is designed to remove specific object types that are not suitable for environmental modeling. Finally, a three-layer CNN is used to get highly representative features of the salient landmarks. In the similarity measurement, a Siamese network is chosen to calculate the similarity between images. Experiments were conducted on three challenging benchmark place recognition datasets and superior performance was achieved compared to other state-of-the-art methods, including FABMAP, SeqSLAM, SeqCNNSLAM, and PlaceCNN. Our method obtains the best results on the precision–recall curves, and the average precision reaches 78.43%, which is the best of the comparison methods. This demonstrates that the CNN features on the screened salient landmarks can be against a strong viewpoint and condition variations.

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“…Thus, CNNs have high potential for use in spatiotemporal fusion technology for remote sensing images. Most previous studies have discussed feature extraction from CNNs [8,9]. However, relatively few studies have discussed CNN algorithms for the spatiotemporal fusion of satellite images.…”

Section: Introduction 1motivationmentioning

confidence: 99%

Spatiotemporal Fusion of Formosat-2 and Landsat-8 Satellite Images: A Comparison of “Super Resolution-Then-Blend” and “Blend-Then-Super Resolution” Approaches

Teo

2021

Remote Sensing

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The spatiotemporal fusion technique has the advantages of generating time-series images with high-spatial and high-temporal resolution from coarse-resolution to fine-resolution images. A hybrid fusion method that integrates image blending (i.e., spatial and temporal adaptive reflectance fusion model, STARFM) and super-resolution (i.e., very deep super resolution, VDSR) techniques for the spatiotemporal fusion of 8 m Formosat-2 and 30 m Landsat-8 satellite images is proposed. Two different fusion approaches, namely Blend-then-Super-Resolution and Super-Resolution (SR)-then-Blend, were developed to improve the results of spatiotemporal fusion. The SR-then-Blend approach performs SR before image blending. The SR refines the image resampling stage on generating the same pixel-size of coarse- and fine-resolution images. The Blend-then-SR approach is aimed at refining the spatial details after image blending. Several quality indices were used to analyze the quality of the different fusion approaches. Experimental results showed that the performance of the hybrid method is slightly better than the traditional approach. Images obtained using SR-then-Blend are more similar to the real observed images compared with images acquired using Blend-then-SR. The overall mean bias of SR-then-Blend was 4% lower than Blend-then-SR, and nearly 3% improvement for overall standard deviation in SR-B. The VDSR technique reduces the systematic deviation in spectral band between Formosat-2 and Landsat-8 satellite images. The integration of STARFM and the VDSR model is useful for improving the quality of spatiotemporal fusion.

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Salient Feature Selection for CNN-Based Visual Place Recognition

Cited by 5 publications

References 21 publications

Efficient Visual Place Recognition by Adaptive CNN Landmark Matching

Efficient Visual Place Recognition by Adaptive CNN Landmark Matching

Robust place recognition based on salient landmarks screening and convolutional neural network features

Spatiotemporal Fusion of Formosat-2 and Landsat-8 Satellite Images: A Comparison of “Super Resolution-Then-Blend” and “Blend-Then-Super Resolution” Approaches

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