SBIR Phase I: Innovative visual search and similarity for decor, apparel, and style

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to develop and commercialize visual search for fine-grained recognition of products and style in interior decor and apparel. The technology will help the broader public find items that may be difficult to search for using traditional text-based search. In many markets (home decor, fashion, etc.), customers seek products that have unique visual appearances that cannot easily be expressed with a text-based search. This project will develop visual search tools for the home decor and apparel markets. This Small Business Innovation Research (SBIR) Phase I project will develop software based on deep learning for product and apparel recognition and style recognition. Our prior prototype uses deep learning to recognize specific products from "regular" photographs taken by customers, where the challenge is that these regular photos of products can have many different backgrounds, sizes, orientations, or lighting when compared to the iconic product image, and the product could be significantly occluded by clutter in the scene. The goal of this project is to generalize the work to achieve broad applicability through four major objectives: Generalizing the settings and product categorization and taxonomy to support a broad range of customers and product types (Objective 1); semi-automatic detection of products in scene images to scale to large photo collections (Objective 2); refining the trained models for fine-grained matches to meet customer needs (Objective 3); and deploying the system live to companies (Objective 4).