Feature Vector Creation Using Hierarchical Data Structure for Spatial Domain Image Retrieval
| Autor: | Sushila Aghav-Palwe, Dhirendra Mishra |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science Feature vector ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Data structure Hierarchical database model Dimension (vector space) Discriminative model Feature (computer vision) Computer Science::Computer Vision and Pattern Recognition General Earth and Planetary Sciences RGB color model Artificial intelligence business Image retrieval General Environmental Science |
| Zdroj: | Procedia Computer Science. 167:2458-2464 |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2020.03.298 |
| Popis: | Building efficient Image Features with optimal dimension and high discriminative power is important in processing the digital information hidden inside the images. Image Retrieval systems use the feature vectors to retrieve similar images based on feature vector similarity. Image Feature Vector generation is important step in CBIR. Most of the CBIR use Linear Data structure for Feature Vector. In this paper the Hierarchical data Structure: Binary Tree is used for Feature vector creation in Image Retrieval Systems. Images in Spatial Domain are considered for Feature Vector Generation. Low-level Feature: Color Descriptors are used as Image Contents, to represents the image in feature vector form. Performance of the image retrieval is tested for Color Images. It has been observed that, along with the Dimensionally reduction, the proposed approach of Feature Vector generation, improves the discriminative power of Feature Vector. For RGB colorspaces the cascaded statistical features, when stores in hierarchical data structure, this provides the facility to perform the calculations in parallel manner also capable to keep discriminative power comparable w.r.t size of feature vector. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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