Content-Based Image Retrieval
The top half of the browser is the search query; the lower half of the browser shows results.
This was a research project I did in 10th grade for high school science fair competitions.
With the astounding number of digital images available, there is an increasing need to search image collections. Many systems such as Google and Flickr use text-based search, but the vast majority of photos (particularly family albums) have no text description available. Content-based image retrieval searches images using visual similarity, rather than text. In this research I investigated methods to improve the performance of image retrieval systems.
I devised an algorithm to automatically combine features using machine learning to improve retrieval accuracy. Perceptual characteristics (color and texture signatures) are extracted as a mathematical representation of images. My system determines the relative importance of color and texture for a given image query to optimize results.
Tech Specs: Color signatures are extracted based on k-means clustering of Lab color space coordinates, and texture signatures are extracted using k-means clustering of Gabor filter dictionaries of 4 scales and 6 orientations. Signature dissimilarities are measured using the Earth Mover’s Distance, and integrated through normalized linear weighting. k-nearest neighbor supervised learning is used to predict weights based on statistical characteristics of color and texture signatures: “color spread” and “texture busyness”.