Texture and colour region separation based image retrieval using probability annular histogram and weighted similarity matching scheme. Issue 7 (27th April 2020)

• Record Type: Journal Article
• Authors: Pradhan, Jitesh; Kumar, Sumit; Pal, Arup Kumar; Banka, Haider
• Is Part Of: IET image processing. Volume 14:Issue 7(2020)
• Page Start: 1303
• Page End: 1315
• Publication Date: 2020-04-27
• Subjects: wavelet transforms -- trees (mathematics) -- image texture -- image colour analysis -- feature extraction -- content‐based retrieval -- probability -- image retrieval -- shape recognition -- image matching -- edge detection -- iterative methods; colour region separation based image retrieval -- content‐based image retrieval system -- CBIR -- primitive image visual features -- colour dominant part -- texture feature -- intensity dominant part -- iterative algorithm -- texture dominant part -- probability‐based semantic centred annular histogram -- unique colour features -- weighted distance‐based feature comparison scheme -- shape feature similarities -- query image -- database images -- image retrieval experiments -- texture image datasets -- retrieval performances -- unique shape feature extraction -- weighted similarity matching scheme -- scale‐invariant feature transform -- two‐dimensional dual‐tree complex wavelet transform -- edge maps; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.6619
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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