
Texture and colour region separation based image retrieval using probability annular histogram and weighted similarity matching scheme
Author(s) -
Pradhan Jitesh,
Kumar Sumit,
Pal Arup Kumar,
Banka Haider
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2018.6619
Subject(s) - pattern recognition (psychology) , artificial intelligence , image retrieval , image texture , content based image retrieval , histogram , computer science , visual word , mathematics , complex wavelet transform , similarity (geometry) , computer vision , feature (linguistics) , wavelet transform , image (mathematics) , image processing , wavelet , discrete wavelet transform , linguistics , philosophy
Content‐based image retrieval (CBIR) uses primitive image features for retrieval of similarimages from a dataset. Generally, researchers extract these visual features fromthe whole image. Therefore, the extracted features contain overlappedinformation of texture, colour, and shape features, and it is a criticalchallenge in the field of CBIR. This problem can be overcome by extracting thecolour features from the colour as well as shape and texture features from theintensity dominant part only. In this study, the authors have proposed aniterative algorithm to separate colour and texture dominant part of the imageinto two different images. Here, a combination of edge maps and gradients hasbeen used to achieve separate colour and texture images. Further,scale‐invariant feature transform and 2D dual‐tree complex wavelet transform hasbeen realised to extract unique shape and texture features from the textureimage. Simultaneously, a probability‐based semantic centred annular histogramhas been suggested to extract unique colour features from the colour image.Finally, a novel weighted distance‐based feature comparison scheme has beenproposed for similarity matching and retrieval. All the image retrievalexperiments have been carried out on seven standard datasets and demonstratedsignificant improvements over other state‐of‐arts CBIR systems