System and method for classifier training and retrieval from classifier database for large scale product identification

A system for automated product identification in a shopping cart, comprising:

• a. a cart having a front wall, a rear wall and two side walls forming an apically open container with a base;
• b. a load cell module operably coupled to the base of the cart;
• c. a plurality of imaging modules coupled to the cart, adapted to image at least one of:
• an item inserted into the cart, and an area of interest outside the cart;
• d. a central processing module in communication with the load cell and the plurality of imaging modules, the central processing module comprising a processor and being in further communication with a non-volatile memory having thereon:
• a classifiers' database;
• a product characteristics database;
• and a processor readable media comprising a set of executable instruction, configured, when executed to cause the processor to retrieve a set of a plurality of classifiers from the classifier database, wherein each set of classifiers is configured to identify a single product inserted into the cart the set of executable instructions is configured, when executed, to cause the processor further to:
• i. using the imaging module, acquire an image of each inserted product;
• ii. preprocess the image of each product inserted to the cart by reducing the number of colors of the product;
• iii. extract a predetermined number of features from the image;
• iv. identify the features extracted, wherein the features extracted by the plurality of imaging module from the inserted product is at least one of:
• color pallet designator wherein the color pallet designator is HSV, CIE-LAB, CIE-XYZ, CIE-LCH or a color pallet designator comprising a combination of the foregoing, a shape designator, volumetric size, weight, a key-word, a graphic element, a scale and rotation invariant feature, and a synthesized deep feature;
• and
• v. produce a color histogram of the image color palette.