Data

Akira Shibata - Putting Together World’s Best Data Processing Research with Python

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• published: 09 Dec 2014
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View slideshare presentation here: http://www.slideshare.net/hijiki_s/pydata-nyc-by-akira-shibata PyData NYC 2014 The talk illustrates how selective-search object recognition and the latest deep-learning object identification algorithm was applied to solving the problem of image cropping. How can you identify the most important part of the image that must not be cropped out when shown as a thumbnail? This is a problem faced by various media and e-commerce where space for the image can be of various sizes and the best portion of the original image must be preserved to maximize effectiveness. Selective search is a new method proposed by Uijlings (U. Amsterdam) et al. which significantly improved on the accuracy of object recognition over the previous exhaustive search methods. This allows us to use advanced methods such as Convolutional Neural Network, a.k.a. deep learning object identification, to identify interesting objects contained in an image. With this, interesting parts of the image can be preserved in the process of cropping producing effective thumbnails. The code for selective search is available for matlab only while the deep learning algorithm, Caffe, is available with a Python wrapper. We will illustrate how Python is best suited to putting together the result of cutting edge research in order to solve complex data processing problems.