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Efficient Content Based Image Retrieval Using Combination Of Dominant-Color , Shape And Texture Features And K-Means Clustering
| Content Provider | Semantic Scholar |
|---|---|
| Author | Amrutkar, Bhagyashri Singh, Lokesh |
| Copyright Year | 2015 |
| Abstract | There is a huge demand for the efficient content based image retrieval system because of the availability of large image databases. In this paper we have present an efficient CBIR framework by extracting the Dominant-color, Texture, edge features and by clustering feature database. We have applied the dominant color extraction using color-quantization technique. Initially the image is divided into some partitions using the color quantization algorithm, here we are dividing into eight partitions and the eight dominant colors are obtained from that partition. Next for shape feature extraction sobel color edge detection technique is used. And local binary pattern (LBP) is performed on gray scale image to extract the texture feature. Then all features discussed above of image are combined to form a single feature vector. Kmeans clustering is applied over combined feature vector of database images. Finally, to retrieve similar images from database similarity matching is performed by Euclidian distance which compares feature vector of clustered database images with that of query image. The result of this proposed approach provides efficient, more accurate result. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://ijecs.in/issue/v4-i12/48%20ijecs.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Algorithm Binary pattern (image generation) Cluster analysis Color quantization Content-based image retrieval Database Edge detection Euclidean distance Feature extraction Feature vector Grayscale Image registration Image texture Index K-means clustering Local binary patterns MATCHING Question (inquiry) Sobel operator statistical cluster |
| Content Type | Text |
| Resource Type | Article |
