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July, 2001

eVision was founded in 1999 to develop visual search technologies. The company has raised a first round of funding; however, details of that round have not been disclosed. eVision has 15 employees.

According to Dr. Mat Malladi, CEO, the Internet is evolving from a text to visual medium. There are over 1 billion images on net today, and 7 million images are added each day. However, current Internet search technologies are limited to performing text and keyword searches. Users typically receive thousands and even hundreds of thousands of results to these types of searches, an unwieldy amount of information. eVision's visual recognition technology provides a means for conducting large-scale searches on rich media content.

According to eVision, although some visual searches do exist today, these competing visual search schemes rely on manual annotation of the images and videos, and when a user conducts a search for rich media, they are still inundated with irrelevant results. Many of those images on the net, Malladi says, are "dollar images," or images directly tied to transactions. "When a consumer searches for a product, they have to rely on text searches, which usually serve up either far too many results, or none at all." Furthermore, Malladi contended that visual searches provide a universal search language, one that is independent of cultural or language variability.

eVision's first product, the eVe SDK, goes beyond text searches, instead relying on visual properties that are automatically extracted from the images and videos based on image processing and pattern recognition techniques. When an image is entered into an eVe database, the image is distilled into representative visual characteristics, based on object, color, texture, and shape. Algorithms are employed that segment an image into distinct object regions and generate scale-independent descriptions of the regions, known as visual signatures. These visual signatures are then organized into an indexing scheme for retrieval. Signatures can be applied to index images, video content, and any digital patterns. This search technique can be applied to the web at large, with pages indexed in a manner similar to that of major search engines.

To establish a media library, image files and video key frames are analyzed, stored, and indexed into media collections. Given an example query image, eVe can search either single or multiple media collections and return a set of sorted images and videos based on visual similarity. The results are ranked according to relevance, and can be further refined.

According to eVision, eVe solves one of the major problems associated with organizing and cataloging visual data by ingesting and tagging image assets automatically. Traditionally, when images are cataloged by attaching keywords, the person cataloging this information has to open up each image one by one and determine which keywords are appropriate. For thousands of images, this can be a time consuming and error prone task. To avoid this, eVe employs Visual MetaTagging, a process that applies text tags to large groups of visually similar assets. This technique helps reduce meta-tagging costs and virtually eliminates errors that grow out of manual tagging. eVe also automatically creates a representative Visual Vocabulary for large image and video databases with vast amounts of data.

Going to market, eVision will first target search engines, providing an extra level of searching capability, and large enterprises, allowing them to better mine their visual assets. The company also says that they are in talks with OEMs to integrate this solution into operating systems and to port it onto chips. "If you have an eVe ASIC in a digital camera, for instance, when you take a picture, that image is automatically indexed, making it searchable at a later time," Peter Giegoldt, VP of Operations said.

Besides digital cameras, the eVe chip is applicable for scanners, camcorders, medical imaging equipment, and satellites to generate automatic searchable signatures as images and videos are captured. Other markets that eVision will target include the media asset management market and verticals, such as medical, brand management, and security.

The eVe solution was in beta when we talked to eVision in mid-June, undergoing testing at North Plains Systems, Ascential Software, Piocon Technologies, and SGI. General availability for eVe was scheduled for late June.

  • Dr. Mat Malladi, CEO and Cofounder (formerly VP of New Business Development at Reliance Industries)
  • Dr. Srinivas Sista, VP of R&D and Cofounder (has more than 10 years' experience in the digital signal/image-processing field)
  • Kasu Sista, VP of Technology and Cofounder (previously a principal consultant at Navigant Consulting and Metzler & Associates)
  • Peter Giegoldt, VP of Operations (formerly a Director at Navigant Consulting)
  • Todd Lohenry, Director of Business Development (previously held senior sales management positions for Apple and CDXC)