Relevance feature mapping for content-based multimedia information retrieval
| Autor: | Guang-Tong Zhou, Yilong Yin, Kai Ming Ting, Fei Tony Liu |
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| Rok vydání: | 2012 |
| Předmět: |
Scheme (programming language)
Information retrieval Computer science Multimedia database Relevance feedback Multimedia information retrieval computer.software_genre Task (project management) Ranking (information retrieval) Ranking Artificial Intelligence Feature (computer vision) Signal Processing Relevance (information retrieval) Computer Vision and Pattern Recognition Data mining computer Software computer.programming_language |
| Zdroj: | Pattern Recognition. 45:1707-1720 |
| ISSN: | 0031-3203 |
| Popis: | This paper presents a novel ranking framework for content-based multimedia information retrieval (CBMIR). The framework introduces relevance features and a new ranking scheme. Each relevance feature measures the relevance of an instance with respect to a profile of the targeted multimedia database. We show that the task of CBMIR can be done more effectively using the relevance features than the original features. Furthermore, additional performance gain is achieved by incorporating our new ranking scheme which modifies instance rankings based on the weighted average of relevance feature values. Experiments on image and music databases validate the efficacy and efficiency of the proposed framework. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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