People could match genres of images to text queries. While early search was still developing-both for text and images-the failures in the methodology didn’t have that much to do with the tech, per se. Searching for “green dress” probably produced results like this: People transfixed by Lopez’s stunning outfit knew what they were seeing, but they didn’t have the detail-designer, event, celebrity-to match it perfectly with surrounding text. Legend has it that specific queries for Jennifer Lopez’s risqué Versace dress were the catalyst.Įven if they weren’t, that idea is right at the heart of how early image search failed users: They wanted a specific picture and had no way to reliably search for it. It was developed because people wanted to get not to approximate images based on the context of web pages but to the exact image that they had in mind. Google Images, which followed in 2001, quickly amassed a huge store of pictures- 250 million within the year. Early image searches like AltaVista’s relied not on the ability to process the image itself and match that to text, but on image descriptions or corresponding text on web pages. This was a proximate way to get people to the pictures they desired. This started in the late ’90s, when AltaVista launched an image feature for its search that allowed you to put in a text term and return images. The search engine would find a matching image by scanning the text of pages that images were on-image titles, descriptions, and any accompanying text-and return something that appeared to match your search, based on its textual context. In the beginning, to find an image, you would come up with some words to describe what you wanted to see-say, “top of Mount Everest.” Then, you’d type those words into search. But for anyone who wants to understand the history of how one of our most used resources-search-has developed to our specific tastes, stick around. You can now search for images with an image, and computers can recognize and categorize images better than ever.īut it’s been a long road from text searches to advanced image recognition, and we’re still developing the technology. Today, we finally have working iterations for this idea. Making image search tech that fits the human model of behavior has involved a long quest focused on providing one thing: giving people the ability to search for images without using words. Only when forced do we add textual attributes-colors, size, shape, texture-to describe images. We see things we want, we save pictures, and we recall images with startling clarity in our mind’s eye. Why? Because people don’t think about images in words. This technique was not cutting the mustard for image search, and search engine developers knew it.
Searching on the web started by imitating analog, text-based searches.
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