Efficient transform-based texture image retrieval techniques under quantization effects

Chaker, Amani; Kaaniche, Mounir; Benazza-Benyahia, Amel; Antonini, Marc

doi:10.1007/s11042-016-4205-5

Cited by 39 publications

(1 citation statement)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The (12) represents the combined histogram of the foreground H F ore ; where α F ore is a weight parameter that can be adjusted to improve the quality of the search.…”

Section: Search Parametersmentioning

confidence: 99%

Efficient histogram for region based image retrieval in the discrete cosine transform domain

Belalia¹,

Belloulata²,

Zhu

2022

IJ-AI

View full text Add to dashboard Cite

<p>Recently, several approaches of content based-image retrieval (CBIR), based on the characteristics of discrete cosine transform (DCT), such as decorrelation and concentration of energy in only a few coefficients, have been proposed. To reduce the semantic gap between high level search and low level patterns, a new concept based on region based search region-based image retrieval (RBIR) has been proposed. Recently, we proposed to use shape-adaptive (SA) DCT in a new RBIR system. In this paper, we propose an efficient histogram optimization suited to our region-based concept. This histogram takes into account the pattern’s from the SA-DCT of the border blocks as well as the DCT coefficients of the internal blocks. Our proposed scheme has greatly improved the results compared to region-based reference methods. Regionbased search is limited to the object of interest only, i.e. a region of the query image can only match a region of another image in the database.</p>

show abstract

“…The (12) represents the combined histogram of the foreground H F ore ; where α F ore is a weight parameter that can be adjusted to improve the quality of the search.…”

Section: Search Parametersmentioning

confidence: 99%