Google's Mood Board Search is a simple web-based Computer Vision tool that lets you teach your computer to recognize visual concepts using mood boards and Machine Learning. Mood Board Search was designed with a primary focus on'Visual Feels,' bringing an abstract and subjective angle to an AI-based visual platform. This platform lets you express your mood and thoughts in a visual format, especially for abstract concepts like'Atmosphere,' 'Duality,' 'Fracture,' or any particular idea or state of mind. The thought behind Mood Board Search was to create an approachable and flexible interface for people without ML expertise, enabling them to train a model and recognize a visual concept. With each mood board, you can train a machine learning model using a technique called Concept Activation Vectors or simply CAV.

In mood board search, researchers used pre-trained computer vision models like GoogLeNet and MobileNet, and a machine learning approach called Concept Activation Vectors (CAVs). CAV is a technique to measure how a trained model is sensitive to the concept presented by the user. The following picture shows how CAV or tested CAV is working.