Image Recognition Using Machine Learning Training Ppt

Slide 1

This slide introduces Image Recognition. Finding objects of interest within an image and determining which category they belong to is known as image recognition.

Instructor’s Notes: Image recognition is a computer vision application that entails tasks such as object detection, image identification, and image categorization.

Slide 2

This slide discusses the working of Image recognition using Machine Learning. Machine Learning techniques are used to glean out hidden knowledge from a dataset of good and bad samples to recognize images.

Instructor’s Notes: When combined with robust AI technology and GPUs, Deep Learning allows for significant advancements in the field of image recognition. Image classification and facial recognition algorithms using Deep Learning reach human-level performance in real-time object detection.

Slide 3

This slide illustrates Machine Learning Image Recognition Models such as Support Vector Machines, Bag of Features Models, and Viola Jones Algorithm.

Instructor’s Notes:

• Support Vector Machines: SVMs function by creating histograms of images that may or may not contain the target items. Next, the program compares the trained histogram values to those of portions of the test picture to see if there are any matches
• Bags of Features Models: Bag of Features models like Scale Invariant Feature Transformation (SIFT) and Maximally Stable Extremal Regions (MSER) function by scanning an image and comparing it to a reference photo of the object to be discovered. The model then pixel matches the features from the sample photo to regions of the target image to see if there are any matches
• Viola Jones Algorithm: Viola-Jones scans people's faces and extracts features, which are then fed into a boosting classifier. As a result, boosted classifiers are created and used to check test photos. A test image must yield a positive outcome from each classifier to find a successful match

Slide 4

This slide depicts the Image Recognition Application for Face Analysis. The video feed of any digital camera or webcam can be used with modern Machine Learning technologies to do simultaneous face detection, face posture estimation, face alignment, gender recognition, smile detection, age estimation,and face recognition.

Instructor’s Notes: Computer vision allows computers to determine identity, intentions, emotional and health status, age, and ethnicity via facial analysis. Some photo recognition software even attempts to use a score to define levels of perceived attractiveness.

Slide 5

This slide discusses the Image identification systems powered by Machine Learning that are used in the agriculture sector. These systems employ cutting-edge tools that have been trained to recognize the type of animal and its behavior.