Image Classification with Hadoop
classification starts with the notion that we build a training set and that
computers are equipped to recognize and categorize what they’re processing at.
In the context, that having greater data helps generate better fraud detection
and risk-predictive models, the same way it also helps system to better
classify images. This requires a significant amount of data processing tools
and resources in software development, however, which would limit or control the scale of deployments.
Image classification is a hot topic in the Hadoop world since no mainstream
technique was empowered — until Hadoop came along — to opening doors for this
kind of expensive processing on such a massive and cost-efficient scale.
this use case, the data set is considered as both the training set and the data
models as the classifiers. Classifiers identify features or specific patterns
within sound, image, or video and classify them accordingly. These are built
and iteratively enhanced from training sets so that the associated precision
scores (a measure of exactness) and recall scores (a measure of coverage) are
measured high. Hadoop is no doubt perfectly suited for image classification
since it provides a huge parallel processing ecosystem to create classifier
models (iterating over training sets) but also enable probably limitless
scalability to process and execute those classifiers across bigger sets of
unstructured data chunks. For instance, social media and multimedia sources
such as YouTube, Facebook, Instagram, and Flickr — these all are sources of
unstructured binary data. We can use Hadoop to scale the processing of massive volumes
of stored images and video for multimedia semantic classification.
this explanation focuses on image analysis, Hadoop can also be implemented in
audio or voice analytics, too. For instance, we could generate a system which
knows how to clearly instantaneously classify the whisper of the wind in
contrast to the whisper of a human voice or to differentiate the sound of human
footsteps running in the perimeter parklands from that of animals and other wildlife
could totally understand that this explanation might have sort of a Star Trek
feel to it, but we can see real implementation explains now. In fact, IBM
builds one of the largest image classification systems in the planet, via the
IBM Multimedia Analysis and Retrieval System (IMARS).
classification has many applications, and being able to compute this
classification at a very huge scale using Hadoop opens up many options for data
analysis as other tools can use this classification information generated for
the images. This technique is very useful to the health industry also. Early
testing has shown this strategy to help reduce the number of missed or
inaccurate diagnoses, saving time, money, and — most of all — lives.