Abstract: A method and system of matching a first product with a second product. The method including converting first product metadata with image metadata and textual data to a first product feature vector. Further, determining a distance between the first product feature vector and a second product feature vector of the second product, the second product feature vector stored in a database of product feature vectors. The distance is compared to a threshold distance, and if the distance is within the threshold distance, validating a match between the first product feature vector and the second product feature vector. The validating further includes geometrically verifying the image metadata of the first product corresponds to image metadata of the second product.

Abstract: A method of product recognition, comprising decomposing a product image of into a set of frequency components; performing a frequency analysis for each frequency component in the set of frequency components; detecting a plurality of edge pixels of the image based on the frequency analysis; selecting a set of interested pixels in the region of each of the plurality of edge pixels; creating a quantized product image; and creating a normalized histogram of the quantized image using the sets of interested pixels selected for each of the plurality of edge pixels, wherein product recognition is based on similarities between the normalized histogram of the quantized image and images of similar products in a database of products.