scholarly journals Introducing Space and Time in Local Feature-Based Endomicroscopic Image Retrieval

2010 ◽  
pp. 18-30 ◽  
Author(s):  
Barbara André ◽  
Tom Vercauteren ◽  
Aymeric Perchant ◽  
Anna M. Buchner ◽  
Michael B. Wallace ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Local Feature ◽  
Space And Time ◽  
Feature Based

scholarly journals Face Recognition from Still Images to Video Sequences: A Local-Feature-Based Framework

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Shaokang Chen ◽  
Sandra Mau ◽  
Mehrtash T. Harandi ◽  
Conrad Sanderson ◽  
Abbas Bigdeli ◽  
...  
Keyword(s):  
Face Recognition ◽  
Local Feature ◽  
Video Sequences ◽  
Still Images ◽  
Feature Based

scholarly journals Performance Evaluation of Single-Label and Multi-Label Remote Sensing Image Retrieval Using a Dense Labeling Dataset

2018 ◽  
Vol 10 (6) ◽  
pp. 964 ◽  
Author(s):  
Zhenfeng Shao ◽  
Ke Yang ◽  
Weixun Zhou
Keyword(s):  
Remote Sensing ◽  
Performance Evaluation ◽  
Deep Learning ◽  
Image Retrieval ◽  
Semantic Segmentation ◽  
Semantic Content ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Benchmark Datasets ◽  
Feature Based

Benchmark datasets are essential for developing and evaluating remote sensing image retrieval (RSIR) approaches. However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representing the most significant semantic content of the image. This is sufficient for simple problems, such as distinguishing between a building and a beach, but multiple labels and sometimes even dense (pixel) labels are required for more complex problems, such as RSIR and semantic segmentation.We therefore extended the existing multi-labeled dataset collected for multi-label RSIR and presented a dense labeling remote sensing dataset termed "DLRSD". DLRSD contained a total of 17 classes, and the pixels of each image were assigned with 17 pre-defined labels. We used DLRSD to evaluate the performance of RSIR methods ranging from traditional handcrafted feature-based methods to deep learning-based ones. More specifically, we evaluated the performances of RSIR methods from both single-label and multi-label perspectives. These results demonstrated the advantages of multiple labels over single labels for interpreting complex remote sensing images. DLRSD provided the literature a benchmark for RSIR and other pixel-based problems such as semantic segmentation.

MF-Re-Rank: A modality feature-based Re-Ranking model for medical image retrieval

2018 ◽  
Vol 69 (9) ◽  
pp. 1095-1108 ◽  
Author(s):  
Hajer Ayadi ◽  
Mouna Torjmen-Khemakhem ◽  
Mariam Daoud ◽  
Jimmy Xiangji Huang ◽  
Maher Ben Jemaa
Keyword(s):  
Image Retrieval ◽  
Medical Image ◽  
Medical Image Retrieval ◽  
Ranking Model ◽  
Feature Based
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