Comparison between K-Mean and C-Mean Clustering for CBIR
| Autor: | Raman Bhati, Durgesh Kumar Mishra, Khushbu Upadhyay, Ritu Shrivastava |
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| Rok vydání: | 2010 |
| Předmět: |
business.industry
Computer science InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION k-means clustering Pattern recognition computer.software_genre Content-based image retrieval Automatic image annotation Similarity (network science) Consensus clustering Visual Word Artificial intelligence Data mining business Cluster analysis Image retrieval computer |
| Zdroj: | 2010 Second International Conference on Computational Intelligence, Modelling and Simulation. |
| DOI: | 10.1109/cimsim.2010.66 |
| Popis: | Traditionally image is retrieved with the help of the associated tag which is added to the image while storing it in the database. This text based image retrieval is time consuming, laborious and expensive. In order to overcome these flaws content based image retrieval is proposed which avoid the use of textual description and retrieve the image based on their visual similarity. To achieve this images are clustered using clustering techniques. Clustering groups similar images based on some properties for efficient and faster retrieval. This paper compares two clustering techniques: K-mean and C-mean clustering used for Content Based Image Retrieval System. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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