1) The document discusses how visual search using image recognition and machine vision is shaping the future of image search and mobile marketing. 2) It highlights Pinterest's recent acquisition of VisualGraph, a company that creates technologies for machine vision, image recognition, and visual search, suggesting virtual search is the future of mobile discovery and shopping. 3) Visual search functions are proving useful for searching images, videos, and other visual content by analyzing visual elements rather than relying on text or metadata, improving search efficiency.

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Visual Search: Shaping the Future of Image Search and Marketing
Mobile phones have changed the digital marketing landscape in that it has forced businesses to modify all their marketing campaigns to adapt not only to the user’s desktop or laptop, but to their handheld devices as well.
Smartphones nowadays have evolved into powerful image and video processing devices complete with high-resolution cameras, rich colour displays, and impressive, high-quality graphics.
This reliance on smartphones, coupled with several news articles and studies, suggest the future of mobile discovery—and ultimately, mobile shopping—lies with the virtual search engine.
Virtual Search and Pinterest’s Purchase
One important supporting factor in the claim that virtual search is the future of mobile is Pinterest’s recent acquisition of VisualGraph, a company that creates machine vision, image recognition, and visual search technologies.
VisualGraph explains, “On Pinterest, millions of people are curating and sharing billions of Pins every day. And these Pins are more than just images – they link to contents that can inspire and enrich people’s lives. We are excited for the opportunity to combine machine vision with human vision and curation, and to build a visual discovery experience that is both aesthetically appealing and immensely useful for people everywhere.”
Pinterest, for its part, explains, “The acquisition of VisualGraph will help us build technology to better understand what people are Pinning. By doing so, we hope to make it easier for people to find the things they love.”
The Old Approach
Today, most users rely on concept-based image indexing. That is, they search for images using descriptions or texts such as keywords, metadata, subject headings, captions, tags, and more. For years, SEO experts and digital marketers have been striving to optimize images to let Google and other search engines understand and properly index visual content and elements.

2. Company Address: 190 Middle Road #10-05 Fortune Centre Singapore 188979
Telephone Number: +65 6336 5352
The New Approach
Content-based image retrieval (CBIR), by contrast, has search engines analysing the visual content of elements rather than its metadata. The function searches for content such as colours, shapes, texture, file type, and any other information that can be derived from the image, rather than relying on text.
Visual search functions are proving to be very useful in searching for images, stock videos, backgrounds, animations, and other visual elements, as it does away with the inefficiencies and limitations inherent with text- or metadata- based image search functions.
Most stock videos, clips, animations, and even basic images were dependent on text descriptions, which can be time-consuming for marketers and SEO specialists. It can also create unintended ambiguities, especially when users do not have exact terms or keywords for an image they are describing. Visual similarity search engine technology does away with all this, which could then be of great help to both mobile and online searches.
Resources:
http://www.visualgraph.com/
http://mashable.com/2014/01/06/pinterest-acquires-visualgraph/
http://www.motionelements.com/animation/video-backgrounds-stock-footage-38
http://abcnews.go.com/Health/wireStory/visual-search-shop-gimmick-game-changing-25168382