Case study
Image clustering
Abstract
The bag-of-words (BoW) model is one of the most popular approaches to image classification and forms an important component of image search systems. The BoW model treats an image’s features as words, and represents the image as a vector of the occurrence counts of image features (words). This chapter discusses the OpenCL implementation of an important component of the BoW model—namely, the histogram builder. We discuss the OpenCL kernel, and study the performance impact of various source code optimizations.
Keywords
image search
feature representation
clustering
histogram
optimization
The bag-of-words (BoW) model is one of the most popular approaches to image classification and forms an important ...
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