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Go Beyond Keywords! Perform a Visual Image Search

Searching graphics files via keywords can be tedious. Learn how to use image-matching technology to find images by matching shapes, patterns, colors, and textures.




Building the Right Environment to Support AI, Machine Learning and Deep Learning

arge collections of diverse images, graphics and video are becoming common, both on the Web and in media asset databases. As the size of the image collections expand, it becomes increasingly difficult to classify and find images matching specific criteria. Current image searches rely on keywords and proximity text to find relevant images. However, such searches invariably miss or misclassify content because they don't search the images (see the Sidebar: Why Text-based Image Searches Are Inadequate). Visual search rectifies that problem by matching color, shape, texture, and 3D shading directly within the image.

What you need:
Win NT4/2k, Solaris 2.7+, Redhat Linux 7 or Mac OS X

The general visual search paradigm is simple. A user selects a sample query image, and then the search engine finds and ranks visually similar images by matching objects and attributes like color, texture and shape. Essentially the user tells the computer: "Find an image that looks similar to this one." For more information, see the Sidebar: How Object-based Visual Searches Work.

Java-based Visual Search toolkit
In this article, you'll see how to create a visual search application using the Java-based eVe Visual Search engine SDK from eVision. The eVe toolkit is free to developers who want to evaluate the API and build prototype applications. The free version limits media collections to 500 images, however, that's sufficient for experimentation and for validating the functionality of visual search on your own image collection. For some ideas, see the Sidebar: Applications for Visual Search.

I've tested the API by searching collections of images from PhotoDisc CDs. The results are exciting.

To create a visual search application with the eVe SDK you follow four basic steps:

  1. Analyze New Images—set the number of segmentation regions, load an image and automatically compute the Visual Signature for an image.
  2. Store Analyzed Images—save a copy of the image, its segmentation map, any metadata and its Visual Signature to a MediaObject and add it to a MediaCollection
  3. Segmentation Map & User Selection—let the user view the segmentation map and select relevant objects to search
  4. Search and Retrieval—select a query image, perform the search, and retrieve and display the resulting images.

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