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Hybrid Algorithm for Image Retrieval using LBG and K-means
| Content Provider | CiteSeerX |
|---|---|
| Author | Chaurasia, Seema Anand Suryawanshi, Vaishali |
| Abstract | In this paper a hybrid algorithm for image retrieval based on texture feature extraction is proposed. Proposed algorithm can be implemented for texture feature retrieval using Vector Quantization (VQ). For texture feature retrieval Linde-Buzo-Gray (LBG) algorithms is used by dividing each image into pixel blocks of size 2X2 where each pixel consists of green, red and blue component. A training vector of dimension 12 can be obtained by putting these in a row. A training set is collection of such training vectors. Size of codebook will be 16X12.In the proposed method K-means algorithm is applied on existing LBG codebook and results are compared with LBG algorithm. From experiments it is found that proposed algorithm gives better relevance percentage as compared to the LBG algorithm. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Image Retrieval Hybrid Algorithm Lbg Algorithm Training Vector Training Set Vector Quantization Pixel Block Method K-means Algorithm Texture Feature Retrieval Linde-buzo-gray Relevance Percentage Blue Component Texture Feature Extraction Texture Feature Retrieval Lbg Codebook Proposed Algorithm |
| Content Type | Text |
| Resource Type | Article |
