.png){kind=icon}
.png){kind=icon}
Return
Improving content-based image retrieval for heterogeneous datasets using histogram-based descriptors
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-017-4708-8.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Image content analysis plays a key role in areas such as image classification, clustering, indexing, retrieving, and object and scene recognition. However, although several image content descriptors have been proposed in the literature, their low performance score or high computational cost makes them unsuitable for content-based image retrieval on large datasets. This paper presents an efficient content-based image retrieval approach that uses histogram-based descriptors to represent color, edge, and texture features, and a k-nearest neighbor classifier to retrieve the best matches for query images. The compactness and speed of the proposed descriptors allow their application in heterogeneous photographic collections whilst showing strong image discrimination in the presence of significant content variation. Experimentation was conducted on four different image collections using four distance metrics. The results show that the proposed approach consistently achieves noteworthy mean average precision, recall, and precision measures. It outperforms state-of-the-art approaches based on the MPEG 7 descriptors (SCD, CLD, and EHD), whilst producing comparable results to those achieved by novel SIFT-based and SURF-based approaches that require more complex data manipulation.
Keywords:
Image retrieval
Visual features
Lab color descriptor
Gabor wavelets
Local binary patterns
Histograms
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3
Papers:
1.9W
Citations:
3.2W