Second, we Star Student
Project quantize the extracted local features from
a query Star Student Project image
into visual words using the duplicate vocabulary in each server
respectively Star Student Project
without inverted indexing . Third, we tackle the issues of both
“What to distribute” and Star Student
Project “How to distribute” from a machine learning
perspective, minimizing the overall search Star Student
Project latency as well as the computational cost
in traversing the inverted indexing files in each server. Finally,
Star Student Projectthe partial
list of ranking images and the ranking scores from each individual
Star Student Project server
are combined to generate the final Star Student
Project or so-called global Star Student
Project image
ranking list and scores.
