Performance Evaluation of Image Retrieval Algorithms using Wavelet-based Feature Extraction: An Experimental Study

2012 ◽  
Author(s):  
Hoang Nguyen-Duc ◽  
Thuong Le-Tien ◽  
Tuan Do-Hong ◽  
Cao Bui-Thu
Keyword(s):  
Experimental Study ◽  
Feature Extraction ◽  
Performance Evaluation ◽  
Image Retrieval ◽  
Retrieval Algorithms

Performance evaluation of web search engines in image retrieval: An experimental study

2021 ◽  
pp. 026666692110102
Author(s):  
Mehrdad (Mozaffar) CheshmehSohrabi ◽  
Elham Adnani Sadati
Keyword(s):  
Experimental Study ◽  
Performance Evaluation ◽  
Image Retrieval ◽  
Search Engine ◽  
Search Engines ◽  
Web Search ◽  
Image Search ◽  
Intense Competition ◽  
Significant Difference ◽  
General Search

This experimental study used a checklist to evaluate the performance of seven search engines consisting of four Image General Search Engines (IGSEs) (namely, Google, Yahoo DuckDuckGo and Bing), and three Image Specialized Search Engines (ISSEs) (namely, Flicker, PicSearch, and GettyImages) in image retrieval. The findings indicated that the recall average of Image General Search Engines and Image Specialized Search Engines was found to be 76.32% and 24/51% with the precision average of 82/08% and 32/21%, respectively. As the results showed, Yahoo, Google and DuckDuckGo ranked at the top in image retrieval with no significant difference. However, a remarkable superiority with almost 50% difference was observed between the general and specialized image search engines. It was also found that an intense competition existed between Google, Yahoo and DuckDuckGo in image retrieval. The overall results can provide valuable insights for new search engine designers and users in choosing the appropriate search engines for image retrieval. Moreover, the results obtained through the applied equations could be used in assessing and evaluating other search tools, including search engines.

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.

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