Scaling Content Based Image Retrieval Systems

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Scaling Content Based Image Retrieval Systems

• Christine Lo, Sushant Shankar, Arun Vijayvergiya
• CS 267

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Motivation

• Finding an efficient way to search for images has
  been increasingly important, especially since
  image databases are growing at an unprecedented
  rate. For example, there are about 550,000 images
  uploaded to Facebook each second. 
• Content Based Image Retrieval (CBIR) offers a way
  to classify images based on implicit criteria
  rather than user generated tags. This will make
  large image databases more organized and more
  searchable.
• The bottleneck of the CBIR system is the
  classification algorithm. k-means is one of the
  classification methods we use for CBIR. We chose
  k-means because it is an unsupervised learning
  technique that will allow us to organize and
  classify unlabeled features.
• Because of the size of image databases such as
  Flickr and Facebook, it is important to scale the
  classification algorithm to handle a large number
  of features. We accomplish this by parallelizing
  the k-means algorithm.

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K-means Algorithm

• General k-means Algorithm
• Takes as input a list of vectors and separates
  them into k clusters.
• Parallelization
• We parallelize the k-means algorithm to minimize
  the computational bottleneck of the CBIR system.
  We compare two implementations of this, an OpenMP
  and an MPI implementation.

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CBIR System
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Results
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Evaluation
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Future Work

• Auto-tune parameters for best results
• Integrate clustering code with CBIR system at
  Berkeley PAR lab
• Test on larger datasets such as Flickr and
  Facebook