How is CogniSign visual search technology different?

Historically, image matching has been done using template matching, which is very computationally intensive. Imagine trying to create a collection of templates of a single object in a color image that would be used as a reference to identify, by using matching techniques, a similar object in another image. Templates would have to be created to show different scale, orientation, viewing perspective, lighting conditions, etc. A massive number of templates would be needed to do a good job. Visual search software solutions designed by our competitors have sidestepped this problem by using indexing techniques. Our competitors typically convert digital images into large sets of numerical attributes (values), which summarize the whole image by measuring the various attributes of the image’s pixels. To perform visual search for similar images, an indexing approach tabulates these attributes from the source image and then seeks out and retrieves images with similar tabulated attributes. Generally speaking, the visual search performance using this approach is not good enough for image and video applications today. Our technology goes back to the template matching approach, with two key innovations: in many use cases, we let the user pick the key features of interest on an object or in an image to drive visual search, narrowing and focusing the search task; and 2) we use a proprietary technology to collect these key features and look for content that is similar to it, in a human like way.

What are the unmet market needs for visual search today?

Visual search technology takes source image content and finds visually similar content in a target database of image or video content. Traditional visual search technology on the market today generally doesn’t have adequate performance – the results in many cases aren’t similar, judged from a human perspective. That’s why there really hasn’t been a break out market leader, though a lot of companies have certainly tried. Getting computers to perform visual search well (like a human) is much harder than it sounds. Additionally, there are some major scalability problems with traditional visual search methods, known as indexing. These approaches worked OK from an IT scalability perspective when the target database to be searched was a few hundred thousand images. Today, many emerging image and video applications require search across millions of images, and traditional technology cannot address those needs. So the major unmet needs are 1) visual search performance is not human like, and 2) scalability is a big obstacle.