A Multiple Instance Learning Framework for Incident Retrieval in Transportation Surveillance Video Databases

2007 ◽  
Author(s):  
Xin Chen ◽  
Chengcui Zhang ◽  
Wei-Bang Chen
Keyword(s):  
Multiple Instance Learning ◽  
Surveillance Video ◽  
Video Databases ◽  
Learning Framework

Semantic retrieval of events from indoor surveillance video databases

2009 ◽  
Vol 30 (12) ◽  
pp. 1067-1076 ◽  
Author(s):  
Chengcui Zhang ◽  
Xin Chen ◽  
Liping Zhou ◽  
Wei-Bang Chen
Keyword(s):  
Semantic Retrieval ◽  
Surveillance Video ◽  
Video Databases

scholarly journals Live Face Verification with Multiple Instantialized Local Homographic Parameterization

2018 ◽  
Author(s):  
Chen Lin ◽  
Zhouyingcheng Liao ◽  
Peng Zhou ◽  
Jianguo Hu ◽  
Bingbing Ni
Keyword(s):  
Facial Expression ◽  
Recall Rate ◽  
Multiple Instance Learning ◽  
Superior Performance ◽  
Face Verification ◽  
Facial Movements ◽  
Learning Framework ◽  
Verification Methods ◽  
Movement Sequence ◽  
Motion Parameterization

State-of-the-art live face verification methods would easily be attacked by recorded facial expression sequence. This work directly addresses this issue via proposing a patch-wise motion parameterization based verification network infrastructure. This method directly explores the underlying subtle motion difference between the facial movements re-captured from a planer screen (e.g., a pad) and those from a real face; therefore interactive facial expression is no longer required. Furthermore, inspired by the fact that ?a fake facial movement sequence MUST contains many patch-wise fake sequences?, we embed our network into a multiple instance learning framework, which further enhance the recall rate of the proposed technique. Extensive experimental results on several face benchmarks well demonstrate the superior performance of our method.

scholarly journals ON THE USE OF MULTIPLE INSTANCE LEARNING FOR DATA CLASSIFICATION

2021 ◽  
Vol 25 (Special) ◽  
pp. 1-127-1-137
Author(s):  
Nibras Z. Salih ◽  
◽  
Walaa Khalaf ◽  
Keyword(s):  
Cross Validation ◽  
Feature Vector ◽  
Multiple Instance Learning ◽  
Support Vector ◽  
Sequential Minimal Optimization ◽  
Bayes Classifier ◽  
Learning Framework ◽  
Single Feature ◽  
Single Instance ◽  
Leave One Out

In the multiple instances learning framework, instances are arranged into bags, each bag contains several instances, the labels of each instance are not available but the label is available for each bag. Whilst in a single instance learning each instance is connected with the label that contains a single feature vector. This paper examines the distinction between these paradigms to see if it is appropriate, to cast the problem within a multiple instance framework. In single-instance learning, two datasets are applied (students’ dataset and iris dataset) using Naïve Bayes Classifier (NBC), Multilayer perceptron (MLP), Support Vector Machine (SVM), and Sequential Minimal Optimization (SMO), while SimpleMI, MIWrapper, and MIBoost in multiple instances learning. Leave One Out Cross-Validation (LOOCV), five and ten folds Cross-Validation techniques (5-CV, 10-CV) are implemented to evaluate the classification results. A comparison of the result of these techniques is made, several algorithms are found to be more effective for classification in the multiple instances learning. The suitable algorithms for the students' dataset are MIBoost with MLP for LOOCV with an accuracy of 75%, whereas SimpleMI with SMO for the iris dataset is the suitable algorithm for 10-CV with an accuracy of 99.33%.

A Highly Scalable and Adaptable Co-Learning Framework on Multimodal Data Mining in a Multimedia Database

2011 ◽  
pp. 170-193
Author(s):  
Zhen Guo ◽  
Christos Faloutsos ◽  
Zhongfei (Mark) Zhang ◽  
Zhongfei (Mark) Zhang
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Multiple Instance Learning ◽  
Multimedia Database ◽  
Multimodal Data ◽  
Learning Framework ◽  
New Information ◽  
Database Indexing ◽  
Constant Operation ◽  
Multimodal Data Mining

This chapter presents a highly scalable and adaptable co-learning framework on multimodal data mining in a multimedia database. The co-learning framework is based on the multiple instance learning theory. The framework enjoys a strong scalability in the sense that the query time complexity is a constant, independent of the database scale, and the mining effectiveness is also independent of the database scale, allowing facilitating a multimodal querying to a very large scale multimedia database. At the same time, this framework also enjoys a strong adaptability in the sense that it allows incrementally updating the database indexing with a constant operation when the database is dynamically updated with new information. Hence, this framework excels many of the existing multimodal data mining methods in the literature that are neither scalable nor adaptable at all. Theoretic analysis and empirical evaluations are provided to demonstrate the advantage of the strong scalability and adaptability. While this framework is general for multimodal data mining in any specific domains, to evaluate this framework, the authors apply it to the Berkeley Drosophila ISH embryo image database for the evaluations of the mining performance. They have compared the framework with a state-of-the-art multimodal data mining method to demonstrate the effectiveness and the promise of the framework.

A Highly Scalable and Adaptable Co-Learning Framework on Multimodal Data Mining in a Multimedia Database

Data Mining ◽  
2013 ◽  
pp. 567-586
Author(s):  
Zhongfei (Mark) Zhang ◽  
Zhen Guo ◽  
Christos Faloutsos ◽  
Jia-Yu Pan
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Multiple Instance Learning ◽  
Multimedia Database ◽  
Multimodal Data ◽  
Learning Framework ◽  
New Information ◽  
Database Indexing ◽  
Constant Operation ◽  
Multimodal Data Mining

This chapter presents a highly scalable and adaptable co-learning framework on multimodal data mining in a multimedia database. The co-learning framework is based on the multiple instance learning theory. The framework enjoys a strong scalability in the sense that the query time complexity is a constant, independent of the database scale, and the mining effectiveness is also independent of the database scale, allowing facilitating a multimodal querying to a very large scale multimedia database. At the same time, this framework also enjoys a strong adaptability in the sense that it allows incrementally updating the database indexing with a constant operation when the database is dynamically updated with new information. Hence, this framework excels many of the existing multimodal data mining methods in the literature that are neither scalable nor adaptable at all. Theoretic analysis and empirical evaluations are provided to demonstrate the advantage of the strong scalability and adaptability. While this framework is general for multimodal data mining in any specific domains, to evaluate this framework, the authors apply it to the Berkeley Drosophila ISH embryo image database for the evaluations of the mining performance. They have compared the framework with a state-of-the-art multimodal data mining method to demonstrate the effectiveness and the promise of the framework.

Classification in the multiple instance learning framework via spherical separation

2019 ◽  
Vol 24 (7) ◽  
pp. 5071-5077 ◽  
Author(s):  
M. Gaudioso ◽  
G. Giallombardo ◽  
G. Miglionico ◽  
E. Vocaturo
Keyword(s):  
Multiple Instance Learning ◽  
Learning Framework ◽  
Spherical Separation
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