An effective fusion model for image retrieval
| Autor: | Leila Mansourian, Mas Rina Mustaffa, Lili Nurliyana Abdullah, Azreen Azman, Muhamad Taufik Abdullah |
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| Rok vydání: | 2017 |
| Předmět: |
Color histogram
Contextual image classification Computer Networks and Communications Color normalization Computer science business.industry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering Pattern recognition 02 engineering and technology Hardware and Architecture Salient Bag-of-words model in computer vision Histogram 0202 electrical engineering electronic engineering information engineering Media Technology 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Image retrieval Software |
| Zdroj: | Multimedia Tools and Applications. 77:16131-16154 |
| ISSN: | 1573-7721 1380-7501 |
| Popis: | In the past decade, the popular Bag of Visual Words approach has been applied to many computer vision tasks, including image classification, video search, robot localization, and texture recognition. Unfortunately, most approaches use intensity features and discard color information, an important characteristic of any image that is motivated by human vision. Besides, if background colors are higher than foreground ones, Dominant Color Descriptor (DCD) retrieves images that contain similar background colors correctly. On the other hand, just color feature extraction is not sufficient for similar objects with different color descriptors (e.g. white dog vs. black dog). To solve these problems, a new Salient DCD (SDCD) color descriptor is proposed to extract foreground color and add semantic information into DCD based on the color distances and salient object extraction methods. Besides, a new fusion model is presented to fuse SDCD histogram and PHOW MSDSIFT histogram. Performance evaluation on several datasets proves that the new approach outperforms other existing, state-of-the-art methods. |
| Databáze: | OpenAIRE |
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