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Trying to search for images based on text and tags sucks. Whether you are tagging and categorizing your personal images, searching for stock photos for your company website, or simply trying to find the right image for your next epic blog post, trying to use text and keywords to describe something that is inherently visual is a real pain.
I faced this pain myself last Tuesday as I was going through some old family photo albums there were scanned and digitized nine years ago.
You see, I was looking for a bunch of photos that were taken along the beaches of Hawaii with my family. I opened up iPhoto, and slowly made my way through the photographs.
It was a painstaking process. The meta-information for each JPEG contained incorrect dates. Perhaps by luck, I stumbled across one of the beach photographs. It was a beautiful, almost surreal beach shot.
Puffy white clouds in the sky. Crystal clear ocean water, lapping at the golden sands.
You could literally feel the breeze on your skin and smell the ocean air. After seeing this photo, I stopped my manual search and opened up a code editor. While applications such as iPhoto let you organize your photos into collections and even detect and recognize faces, we can certainly do more.
It would allow you to apply visual search to your own images, in just a single click. I spent the next half-hour coding and when I was done I had created a visual search engine for my family vacation photos. I then took the sole beach image that I found and then submitted it to my image search engine.
Within seconds I had found all of the other beach photos, all without labeling or tagging a single image. Looking for the source code to this post?
Sounds pretty hard to do, right? I mean, how do you quantify the contents of an image to make it search-able? In general, there tend to be three types of image search engines: Search by Meta-Data Figure 1: Example of a search by meta-deta image search engine. Notice how keywords and tags are manually attributed to the image.
Searching by meta-data is only marginally different than your standard keyword-based search engines mentioned above. Search by meta-data systems rarely examine the contents of the image itself.
Instead, they rely on textual clues such as 1 manual annotations and tagging performed by humans along with 2 automated contextual hints, such as the text that appears near the image on a webpage.
When a user performs a search on a search by meta-data system they provide a query, just like in a traditional text search engine, and then images that have similar tags or annotations are returned.
Again, when utilizing a search by meta-data system the actual image itself is rarely examined. A great example of a Search by Meta-Data image search engine is Flickr.
After uploading an image to Flickr you are presented with a text field to enter tags describing the contents of images you have uploaded. Flickr then takes these keywords, indexes them, and utilizes them to find and recommend other relevant images.
Search by Example Figure 2: The contents of the image itself are used to perform the search rather than text. Search by example systems, on the other hand, rely solely on the contents of the image — no keywords are assumed to be provided.
The image is analyzed, quantified, and stored so that similar images are returned by the system during a search. A great example of a Search by Example system is TinEye. TinEye is actually a reverse image search engine where you provide a query image, and then TinEye returns near-identical matches of the same image, along with the webpage that the original image appeared on.
Take a look at the example image at the top of this section. Here I have uploaded an image of the Google logo. Are you going to manually label each of these 6 billion images in TinEye? That would take an army of employees and would be extremely costly. Then, when a user submits a query image, you extract features from the query image and compare them to your database of features and try to find similar images.
These types of systems tend to be extremely hard to build and scale, but allow for a fully automated algorithm to govern the search — no human intervention is required.The dtSearch product line can instantly search terabytes across an Internet or Intranet site, network, desktop or mobile device.
•. In this tutorial, you'll uncover my complete guide to building an image search engine (CBIR system) using Python and OpenCV from start to finish. Real news, curated by real humans. Packed with the trends, news & links you need to be smart, informed, and ahead of the curve.
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