The Assistive System Progress Report 2 Shifali Kumar Bishwo Gurung

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THE ASSISTIVE SYSTEM SHIFALI KUMAR BISHWO GURUNG JAMES CHOU

Advisor: Dr.vahidipour Zahra salimian Shaghayegh jalali Dec 2017

Outlines Introduction & Objectives Methodology & Workflow

Presentation transcript:

The Assistive System Progress Report 2 Shifali Kumar Bishwo Gurung James Chou

New Additions Use of local and global features Datastore of image categories (Caltech 101 Categories) Training classifiers (SVM, random forest) Text-to-speech synthesizer

Histogram of Oriented Gradients Uses global features to describe the image. Typically used for human detection. Used with SVM for image classification.

Histogram of Oriented Gradients (Example)

Code Structure

Support Vector Machine A probabilistic binary linear classifier which is a part of machine learning It builds a learning model that assigns new examples into certain categories It makes classification decision based on the value of linear combinations of the characteristics; making features presented to the machine into feature vectors (ex. pixels)

The Classifier ASSEMBLE - Create image data set of three categories TRIM - Make the number of images equal PARTITION - Divide the data set into training and validation sets TRAIN - Create a model for future sets TEST - Predict the possible category where the new image belongs

Sample Categories

BagofFeatures Model

Classifier Data Average Accuracy Category Starfish Rhino Cup Count 86 52 57 Average Accuracy Training Set: 90% (works with collected features from old images) Validation Set: 70% (uses old data to use for new data)

Results Training Set Validation Set

Code Snippets imgSets = partition(imgSets, minSetCount, 'randomize'); [trainingSets, validationSets] = partition(imgSets, 0.3, 'randomize'); % input an image to be classified into the proper category img = imread(fullfile(rootFolder, 'unknown', 'image_0001.jpg')); [labelIdx, scores] = predict(categoryClassifier, img); % Display the string label categoryClassifier.Labels(labelIdx)

HOG with SVM

Sample Results ANS: RHINO ANS: STARFISH ANS: STARFISH

Random Forest Functions as an object classifier A technique of machine learning using an ensemble of decision trees More stable by averaging trees that grow irregularly to give less variance Has improved predictive performance by providing less random errors Utilizes the random subspace method

Random Forest

Conclusion Observed different methods of extracting features from images. The running time with BagofFeatures model took longer than expected. Hopes of random forest outperforming in speed and accuracy. Speech-to-text feature is next.

Any questions and concerns are welcome. Thank you! Any questions and concerns are welcome.