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Figure 17 The results of recovery with GS-PhSparse and GS for large-scale retrieval problem.
This method can reduce the sensitivity of changing the training sets or real-world large-scale retrieval where images are added to the database with time.
Extracting a high number of patches per image (for instance, following a dense sampling strategy) could make a method more robust but demands extensive use of memory and storing resources, making it impractical for large-scale retrieval scenarios.
In large-scale retrieval application, high-dimensional global image representations are often dimensionally reduced to short image representations by PCA, which can be further encoded in compact codes using binarization or product quantization techniques.
Recently, the bag-of-features (BoF) representation has attracted substantial interest in large scale image retrieval.
To design a large scale parallel information retrieval system, both performance and storage cost has to be taken into integrated consideration.
To investigate on a larger scale the retrieval stability to c changes, we computed for each method the overlap between sets of top 10 terms from two different c's for the w sets mentioned in 'Evaluating accuracy of P-values' and then took the average (Fig. 2B).
Image annotation plays a significant role in large scale image understanding, indexing and retrieval.
The common practice [19, 21, 39, 51, 52] to evaluate large-scale image retrieval performance is to employ a large image database as distractors included in the retrieval database.
Figure 16 The results of recovery with RAAR-PhSparse and RAAR for large-scale phase retrieval problem.
Image representation is one of the key issues for large-scale image retrieval applications.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com