Image Retrieval using One-Dimensional Color Histogram Created with Entropy
| Autor: | Recep Demirci, Ufuk Tanyeri, Mahmut Kılıçaslan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Color histogram
lcsh:Computer engineering. Computer hardware General Computer Science Computer science 020209 energy Feature vector Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION lcsh:TK7885-7895 02 engineering and technology image retrieval Histogram Computer Science::Multimedia 0202 electrical engineering electronic engineering information engineering Entropy (information theory) Electrical and Electronic Engineering Image retrieval business.industry feature extraction 020208 electrical & electronic engineering Vector quantization Pattern recognition ComputingMethodologies_PATTERNRECOGNITION vector quantization Computer Science::Computer Vision and Pattern Recognition Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business entropy lcsh:TK1-9971 histograms |
| Zdroj: | Advances in Electrical and Computer Engineering, Vol 20, Iss 2, Pp 79-88 (2020) |
| ISSN: | 1844-7600 1582-7445 |
| Popis: | Image histograms are frequently used as a feature vector in content-based image retrieval (CBIR). The related methodology involves processing of a single channel histogram on gray level images while histograms of three channels must be processed in color images. Subsequently, there are two ways to process histograms of color images. In the first approach, the length of feature vector is extended by adding histogram data of each channel to create new feature vector. However, this kind of solution increases computational time and complexity. Second solution is to combine the histogram data obtained from each channel to establish a feature vector. In this study, a novel image retrieval approach, which uses a cluster-based one-dimensional histogram (ODH) for color images has been developed. Initially, multiple thresholds (MT) for each channel were calculated by means of Kapur entropy method. Then, the RGB color space was subdivided into sub-cubes or prisms. The numbers of pixels in each cluster and cluster index or class label have been used to construct a cluster-based one-dimensional histogram. Finally, image retrieval process has been implemented by using the one-dimensional color histogram (ODH) of images in database and query. |
| Databáze: | OpenAIRE |
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