Related Experiment Videos
A multimedia retrieval framework based on semi-supervised ranking and relevance feedback.
Yi Yang1, Feiping Nie, Dong Xu
1College of Computer Science, Zhejiang University, Hangzhou, China. yiyang@cs.cmu.edu
IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 17, 2011
Summary
This study introduces a new framework for multimedia analysis and retrieval using two algorithms: ranking with Local Regression and Global Alignment (LRGA) for data ranking and a semi-supervised long-term Relevance Feedback (RF) for refining data representation.
Area of Science:
- Computer Science
- Machine Learning
- Information Retrieval
Background:
- Multimedia content analysis and retrieval is crucial for organizing and accessing large datasets.
- Existing methods often struggle with robustness, scalability, and cross-media applications.
Purpose of the Study:
- To develop a novel framework for enhanced multimedia content analysis and retrieval.
- To introduce two independent, semi-supervised algorithms for improved data ranking and representation refinement.
Main Methods:
- Proposed a semi-supervised algorithm, ranking with Local Regression and Global Alignment (LRGA), to learn a robust Laplacian matrix for data ranking.
- Developed a semi-supervised long-term Relevance Feedback (RF) algorithm that utilizes data distribution and user feedback for refining multimedia data representation.
- Formulated and solved a trace ratio optimization problem for the RF algorithm.
Main Results:
- The LRGA algorithm effectively assigns optimal ranking scores by globally aligning local regression models.
- The long-term RF algorithm refines multimedia data representation by considering feature space distribution and historical feedback.
- Experiments on four datasets demonstrated advantages in precision, robustness, scalability, and computational efficiency for various retrieval tasks.
Conclusions:
- The proposed framework offers a significant advancement in multimedia content analysis and retrieval.
- The two algorithms, LRGA and long-term RF, provide robust and efficient solutions for data ranking and representation refinement.
- The framework shows strong performance in cross-media retrieval, image retrieval, and 3D motion/pose data retrieval.
Related Concept Videos
01:12Retrieval
Retrieval is the process of getting information out of memory storage and back into conscious awareness. This ability is essential for daily tasks like brushing hair and teeth, driving to work, and performing job duties. Retrieval occurs in three ways: recall, recognition, and relearning.
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
Recall involves accessing information without cues, such as during an essay test, where individuals must retrieve facts and concepts from memory unaided. Another example is remembering the name of a colleague...
01:45ER Retrieval Pathway
In the secretory pathway, vesicles transport proteins from one cellular compartment to another in forward transport to deliver the protein to its correct location. Occasionally, misfolded proteins and incorrect proteins escape their original compartments, and a retrieval pathway is used to return the escaped proteins to their original compartment.
The ER uses many checkpoints to prevent the entry of incorrectly folded or a resident protein as cargo onto a transport vesicle. These mechanisms...
The ER uses many checkpoints to prevent the entry of incorrectly folded or a resident protein as cargo onto a transport vesicle. These mechanisms...
01:02Ranks
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...