Computer Vision News - February 2017

Computer Vision News Tool 19 MapReduce: Matlab's MapReduce function is similar to the popular Hadoop MapReduce. MapReduce uses a Datastore to subdivide data into smaller blocks that fit into memory. Each block of data which needs to be processed is formatted by the Map phase. Then, the processed data blocks go through a Reduce phase, which aggregates it to produce a final result. This example shows how to use ImageDatastore and MapReduce to find images with maximum hue, saturation and brightness values in an image collection. Prepare Data: Create a Datastore using the folder paths, and filter which images are included in the Datastore using the FileExtensions Name-Value pair. Run MapReduce: Use MapReduce to apply the map and reduce functions to the ImageDatastore, ds. maxHSV =MapReduce (ds, @hueSaturationValueMapper, @hueSaturationValueReducer); where: • hueSaturationValueMappe - is a function that stores the image data and the average HSV values as intermediate values. • hueSaturationValueReducer - is a function that receives a list of the image file names along with the respective average HSV values and finds the overall maximum values of average hue, saturation and brightness values. Tool ds = imageDatastore({demoFolder, imsciFolder}, ... 'FileExtensions', {'.jpg', '.tif', '.png'}); “Bag of words constructs a "vocabulary" of features representative of each image category”