It seems that the information retrieval effectiveness of Google on reverse image search needs to be improved. If the categories are ignored when more images were evaluated, the performance of displaying relevant images in higher ranks increased, whereas the performance of retrieving relevant images decreased.

The ANR range is from 47.21% (Toys-cut-off point 20) to 71.31% (Computer-cut-off point 100).

The AP range is from 41.60% (Toys-cut-off point 100) to 71% (Home-cut-off point 20). At the cut-off points 20, 40, 60, 80, and 100, both APs and ANRs were calculated for each category and for all 25 IQs. These 25 IQs were run on the search engine, and for each, the first 100 images retrieved were evaluated with binary relevance judgment. Five fresh IQs with different main concepts were created for each of the five categories.

This study investigates the reverse image search performance of Google, in terms of Average Precisions (APs) and Average Normalized Recalls (ANRs) at various cut-off points,on finding out similar images by using fresh Image Queries (IQs) from the five categories “Fashion,” “Computer,” “Home,” “Sports,” and “Toys.” The aim is to have an insight about retrieval effectiveness of Google on reverse image search and then motivate researchers and inform users.

Google reverse image lookup