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Are Large Scale Training Images or Discriminative Features Important for Codebook Construction?

Advances in machine learning and image feature representations have led to great progress in pattern recognition 
 
approaches in recognising up to 1000 visual object categories. However, the human brain solves this 
 
problem effortlessly as it can recognise about 10000 to 100000 objects with a small number of examples. In 
 
recent years bag-of-features approach has proved to yield state-of-the-art performance in large scale evaluations. 
 
In such systems a visual codebook plays a crucial role. For constructing a codebook researchers cover 
 
a large-scale of training image set. But this brings up the issue of scalability. A large volume of training data 
 
becomes difficult to process whereas the high dimensional image representation could make many machine 
 
learning algorithms become inefficient or even a breakdown. In this work we investigate whether the dominant 
 
bag-of-features approach used in object recognition will continue significantly to improve with large training 
 
image set or not. We have validated a one-pass clustering algorithm to construct visual codebooks for object 
 
classification tasks on the PASCAL VOC Challenge image set. Our testing results show that adding more 
 
training images do not contribute significantly to increase the performance of classification but it increases 
 
the overall model complexity in terms of increased storage requirement and greater computational time. This 
 
study further suggests an alternative view to the community working with the patch-based object recognition 
 
to enforce retaining more discriminative descriptors rather than the reminiscent of the BIG data hypothesis.