Uniform Extended Local Ternary Pattern for Content Based Image Retrieval
| Autor: | Mihai Mocanu, Faiq Baji |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology Image segmentation Content-based image retrieval 01 natural sciences Texture (geology) 010305 fluids & plasmas Image (mathematics) Histogram 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Medical imaging 020201 artificial intelligence & image processing Artificial intelligence business Image retrieval |
| Zdroj: | 2018 22nd International Conference on System Theory, Control and Computing (ICSTCC). |
| DOI: | 10.1109/icstcc.2018.8540712 |
| Popis: | The retrieval of images depending on content is a recurrent research topic in medical imaging. Most CBIR systems are designed to help physicians in the diagnostic of the pathologies. Image retrieval according to the content of the texture features can be performed through various methods developed so far. Local texture features are very beneficial for the analysis of the texture, thus, they are extensively used in image retrieval. The original LBP is improved in this paper with a new addition for CBIR called uniform extended local ternary pattern (UELTP). The method decomposes the image into objects; local texture features are extracted and stored into n-dimensional texture feature vectors. Then, the images are frequently obtained from a huge database dedicated for images using these vectors. In this paper, the performance of LBP descriptor, LTP and ELTP are evaluated for CBIR. According to the results, uniform extended local ternary pattern more accurate than other descriptors in terms of image retrieval. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|