'How to Build an Image Search Engine to Find Similar Images' computervision machinelearning
you have to know the total number of relevant ones to the request answers the question of whether found relevant in principle among the top-kShows how densely we fill the top of the search results with relevant examples. You can look at this as the amount of information received by the user of the search engine, which has read the least number of pages. Accordingly, the greater the amount of information to the number of pages read, the higher the metric.
Input: image requests and images relevant to them. There is markup in the form of a list relevant to this query. To calculate metrics, you can calculate the relevance matrix each with each and, based on the binary information about the relevance of the elements, calculate the metrics.Input: image requests, and images relevant to them. There should also be a validation database of images, in which, ideally, all relevant queries are marked. Also, it should not contain query images, otherwise, they will have to be cleaned at the search stage so that they do not clog the top-1.
To calculate metrics, you can go through all the requests, calculate the distances to all elements, including relevant ones, and send them to the metrics calculation function.Some companies argue with other companies that the latter should not use the pictorial elements of the former's brand. In such cases, a weaker manufacturer tries to parasitize on the success of a large brand by presenting its products and services under similar symbols.
system. In such systems, a search for similar images will be a good helper - the expert will be able to find analogs faster.The size of the indexed database of images: several million trademarks. Here the first image is a query, on the next line is a list of expected relevant ones, the rest of the lines are what the search engine gives out in order of decreasing relevance.That's all. It was overview material.
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