Big Data Analytics and Image Processing - PowerPoint Presentation

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Big Data Analytics and Image Processing

Divya Spandana

Marneni

Agenda

What is Big Data

Big Data and image processing

Why to analyze big images

Complexity involved in processing

Hadoop Image processing framework

Image Retrieval in big data

Feature extraction

Applications

What is Big Data

Huge amount of data in the order of terabytes or petabytes.

Difficult to analyze using traditional data management tools.

Characterized by Volume, Variety, Velocity

Structured, unstructured or semi-structured

Some figures

Facebook ingests 500 TB of new data everyday.

Boeing 737 generates 240 TB of flight data during a single flight across US.

Big Data in Image Processing

80 percent of big data consists of images and videos.

Source

Social networking sites, surveillance cameras, satellite images, web image collection, medical data, drones.

Big images could mean two things:

A single large image

Ex: Giga-pixel and Tera-pixel images -

http://360gigapixels.com/london-320-gigapixel-panorama/ A dataset of large number of small imagesEx: a massive set of millions of images available with sites like Flicker, Instagram etc.

Why to analyze?

By analyzing the data we develop some insights.

Identify patterns and general trends.

Will be used for improving operational efficiency.

Gain revenues and competitive advantages.

Predict the future and take necessary steps.

Better decision making.

Scope for new products and services.

Complexity involved in processing

Requires lot of computational power, network bandwidth and storage.

Also, the complexity lies in developing efficient algorithms that can be scalable.

The relations among multimedia data and the underlying topics are more complex.

Visualizing complex relations is crucial to understanding the implications that the data has.

When the amount of data scales to terabytes or petabytes processing by traditional methods fail.

Approaches in processing big images

Programming constructs

Streaming

Block processing

Parallel for-loops

GPU Arrays

Distributed arrays

MapReducePlatformsDesktop(Multicore, GPU)ClustersCloud Computing Hadoop

Hadoop

This frameworks provides a platform for:

Intensive data processing

Distributed storage

Ability to store and process huge amounts of any kind of data, quickly.

Computing power.

Fault tolerance

Flexibility.Low costScalability.

Hadoop Image Processing Framework

HDFS

HBASE

MapReduce

Feature Extraction

HDFS

A distributed file system that offers operations similar to traditional file system (create, move, delete, remove)

Stores data reliably even at the time of failures.

HBASE

This is the

NoSql

database used by Hadoop.

Key/Value store with deep analytic capabilities.Meant to host large tables with billions of rows and millions of columns.MapReduceParallel processing framework for processing large amount of data.Decomposes work submitted by a client into map and reduce jobs.

<

Key,value

> data model of map-reduce

A list of key, value pairs is given as input to the mappers.

A list of intermediate key, value pairs is produced.

Reducers combine the key, value pairs from intermediate steps and finally give a set of values that are there for a given key.

Image features represent the keys and images files are given as values.

Image Retrieval

Image matching and retrieval.

Text based image retrieval

Content based image retrieval

Text based image retrieval

Annotation of images with text.

Based on keywords, captions, description, html codes etc.Ex : Input – dog

Content based image retrieval (CBIR)

Based on image features rather than the text assigned to images.

Features include color, shape, texture

Feature Extraction

Image characteristics could be color, texture and shape.

There are three main steps in content based image retrieval

Selecting appropriate image characteristics.

Adopting the effective feature extraction method.

Using an effective feature matching algorithm.

Similarity of images is given by the

distance between the features.

Feature extraction – Using Color Moments

The goal is to extract all images whose color composition is similar to a query image.

A query could be an image or a feature value.

Extraction of feature vector from image stored in image database.

Color moments – mean, standard-deviation, skewness

Regions and color channels

Color moments indexing technique

An image is given as input.

Partition the image into three equal non-overlapping regions.

Calculate the color moments for this image - F

r2,i

,

pr2,i, tr2,iSimilarly calculate the color moments for the query image - Er2,i, σr2,i, Sr2,IIf we consider H as query image and I as input image, the similarity of two regions can be defined by :

Color moments technique - continued

Total similarity between two images given by H and I is given by :

The color feature vectors for query image and the input image are given by:

Query image –

Input Image -

Distance between these features is given by -Canberra distance:

We calculate the color moments for all images in the dataset and apply the above formula to get the similarity values “d”.

Store them in an array.The array is sorted in ascending order and first element of d corresponds to the most similar image.

Hadoop Image Processing framework - revisited

Image are stored in various node using HDFS.

The image information like image path, size, URL’s are stored in HBASE in row and column format.

Compute the extracted features using a feature extraction method.

Store in HBASE in the form of <

feature#,file

#>

Mappers will be mapping query image <feature#,file#> to all vector files of images in image store.Grouping, sorting and shuffling of intermediate key are performed.

Reducers then merge all the intermediate key value pairs and output the final result.

The result is sent to the end user.

Applications

Searching and browsing large image and video archives.

Ex : Google and Yahoo

Photo and video sharing

Ex:

Youtube

and

flickrImage retrieval in healthcare domain for decision assisting process. Ex. Optical biopsy system imagesProcessing of pictures from surveillance cameras for face detection.

References

R.Datta

, D. Joshi,

J.Li

, J.Z. Wang, " Image retrieval: ideas, influences, and trends of the new age ", ACM Computing Surveys 40(2), 2008, pp. 1-60

S.

Mangijao

Singh , K. Hemachandran, Content Content-Based Image Retrieval using Color Moment and Gabor Texture leature, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 5, No 1, September 2012 ISSN (Online): 1694-0814.Christopher Crick, Sridhar Vemula, Hadoop Image Processing FrameworkSeyyed Mojtaba Banaei, Hossein Kardan Moghaddam, Hadoop and Its Role in Modern Image ProcessingYAO Qing-An, ZHENG Hong, XU Zhong-Yu, WU

Qiong

, LI

Zi

-Wei, and Yun

Lifen

, Massive Medical Images Retrieval System Based on Hadoop

Thank you…..