Real-time, large-scale duplicate image detection method based on multi-feature fusion

2016 ◽  
Vol 13 (3) ◽  
pp. 557-570 ◽  
Author(s):  
Ming Chen ◽  
Yuhua Li ◽  
Zhifeng Zhang ◽  
Ching-Hsien Hsu ◽  
Shangguang Wang
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Detection Method ◽  
Feature Fusion ◽  
Image Detection ◽  
Duplicate Image Detection

scholarly journals Erratum to: Real-time, large-scale duplicate image detection method based on multi-feature fusion

2017 ◽  
Vol 16 (5) ◽  
pp. 1881-1881
Author(s):  
Ming Chen ◽  
Yuhua Li ◽  
Zhifeng Zhang ◽  
Ching-Hsien Hsu ◽  
Shangguang Wang
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Detection Method ◽  
Feature Fusion ◽  
Image Detection ◽  
Duplicate Image Detection

scholarly journals SSD7-FFAM: A Real-Time Object Detection Network Friendly to Embedded Devices from Scratch

2021 ◽  
Vol 11 (3) ◽  
pp. 1096
Author(s):  
Qing Li ◽  
Yingcheng Lin ◽  
Wei He
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Large Scale ◽  
Feature Fusion ◽  
Contextual Information ◽  
Attention Mechanism ◽  
Detection Accuracy ◽  
Single Shot ◽  
Feature Maps ◽  
Embedded Devices

The high requirements for computing and memory are the biggest challenges in deploying existing object detection networks to embedded devices. Living lightweight object detectors directly use lightweight neural network architectures such as MobileNet or ShuffleNet pre-trained on large-scale classification datasets, which results in poor network structure flexibility and is not suitable for some specific scenarios. In this paper, we propose a lightweight object detection network Single-Shot MultiBox Detector (SSD)7-Feature Fusion and Attention Mechanism (FFAM), which saves storage space and reduces the amount of calculation by reducing the number of convolutional layers. We offer a novel Feature Fusion and Attention Mechanism (FFAM) method to improve detection accuracy. Firstly, the FFAM method fuses high-level semantic information-rich feature maps with low-level feature maps to improve small objects’ detection accuracy. The lightweight attention mechanism cascaded by channels and spatial attention modules is employed to enhance the target’s contextual information and guide the network to focus on its easy-to-recognize features. The SSD7-FFAM achieves 83.7% mean Average Precision (mAP), 1.66 MB parameters, and 0.033 s average running time on the NWPU VHR-10 dataset. The results indicate that the proposed SSD7-FFAM is more suitable for deployment to embedded devices for real-time object detection.

scholarly journals Near-Duplicate Image Detection System Using Coarse-to-Fine Matching Scheme Based on Global and Local CNN Features

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 644 ◽  
Author(s):  
Zhili Zhou ◽  
Kunde Lin ◽  
Yi Cao ◽  
Ching-Nung Yang ◽  
Yuling Liu
Keyword(s):  
Real Time ◽  
High Efficiency ◽  
Feature Matching ◽  
Saliency Detection ◽  
Great Success ◽  
Feature Maps ◽  
Image Detection ◽  
Global And Local ◽  
Coarse To Fine ◽  
Duplicate Image Detection

Due to the great success of convolutional neural networks (CNNs) in the area of computer vision, the existing methods tend to match the global or local CNN features between images for near-duplicate image detection. However, global CNN features are not robust enough to combat background clutter and partial occlusion, while local CNN features lead to high computational complexity in the step of feature matching. To achieve high efficiency while maintaining good accuracy, we propose a coarse-to-fine feature matching scheme using both global and local CNN features for real-time near-duplicate image detection. In the coarse matching stage, we implement the sum-pooling operation on convolutional feature maps (CFMs) to generate the global CNN features, and match these global CNN features between a given query image and database images to efficiently filter most of irrelevant images of the query. In the fine matching stage, the local CNN features are extracted by using maximum values of the CFMs and the saliency map generated by the graph-based visual saliency detection (GBVS) algorithm. These local CNN features are then matched between images to detect the near-duplicate versions of the query. Experimental results demonstrate that our proposed method not only achieves a real-time detection, but also provides higher accuracy than the state-of-the-art methods.

scholarly journals A Lightweight Object Detection Framework for Remote Sensing Images

2021 ◽  
Vol 13 (4) ◽  
pp. 683
Author(s):  
Lang Huyan ◽  
Yunpeng Bai ◽  
Ying Li ◽  
Dongmei Jiang ◽  
Yanning Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Real Time ◽  
Large Scale ◽  
Feature Fusion ◽  
Computational Cost ◽  
Feature Representation ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Low Level

Onboard real-time object detection in remote sensing images is a crucial but challenging task in this computation-constrained scenario. This task not only requires the algorithm to yield excellent performance but also requests limited time and space complexity of the algorithm. However, previous convolutional neural networks (CNN) based object detectors for remote sensing images suffer from heavy computational cost, which hinders them from being deployed on satellites. Moreover, an onboard detector is desired to detect objects at vastly different scales. To address these issues, we proposed a lightweight one-stage multi-scale feature fusion detector called MSF-SNET for onboard real-time object detection of remote sensing images. Using lightweight SNET as the backbone network reduces the number of parameters and computational complexity. To strengthen the detection performance of small objects, three low-level features are extracted from the three stages of SNET respectively. In the detection part, another three convolutional layers are designed to further extract deep features with rich semantic information for large-scale object detection. To improve detection accuracy, the deep features and low-level features are fused to enhance the feature representation. Extensive experiments and comprehensive evaluations on the openly available NWPU VHR-10 dataset and DIOR dataset are conducted to evaluate the proposed method. Compared with other state-of-art detectors, the proposed detection framework has fewer parameters and calculations, while maintaining consistent accuracy.

scholarly journals DeepFireNet: A real-time video fire detection method based on multi-feature fusion

2020 ◽  
Vol 17 (6) ◽  
pp. 7804-7818
Author(s):  
Bin Zhang ◽  
◽  
Linkun Sun ◽  
Yingjie Song ◽  
Weiping Shao ◽  
...  
Keyword(s):  
Real Time ◽  
Detection Method ◽  
Feature Fusion ◽  
Fire Detection

scholarly journals A Web Shell Detection Method Based on Multiview Feature Fusion

2020 ◽  
Vol 10 (18) ◽  
pp. 6274
Author(s):  
Tiantian Zhu ◽  
Zhengqiu Weng ◽  
Lei Fu ◽  
Linqi Ruan
Keyword(s):  
Large Scale ◽  
Detection Method ◽  
Feature Fusion ◽  
Information Leakage ◽  
Detection Methods ◽  
Support Vector ◽  
Sensitive Information ◽  
Fisher Score ◽  
Multiple Levels ◽  
Traffic Behavior

Web shell is a malicious script file that can harm web servers. Web shell is often used by intruders to perform a series of malicious operations on website servers, such as privilege escalation and sensitive information leakage. Existing web shell detection methods have some shortcomings, such as viewing a single network traffic behavior, using simple signature comparisons, and adopting easily bypassed regex matches. In view of the above deficiencies, a web shell detection method based on multiview feature fusion is proposed based on the PHP language web shell. Firstly, lexical features, syntactic features, and abstract features that can effectively represent the internal meaning of web shells from multiple levels are integrated and extracted. Secondly, the Fisher score is utilized to rank and filter the most representative features, according to the importance of each feature. Finally, an optimized support vector machine (SVM) is used to establish a model that can effectively distinguish between web shell and normal script. In large-scale experiments, the final classification accuracy of the model on 1056 web shells and 1056 benign web scripts reached 92.18%. The results also surpassed well-known web shell detection tools such as VirusTotal, ClamAV, LOKI, and CloudWalker, as well as the state-of-the-art web shell detectionmethods.

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