Category: Visual Search

Image Search by Image Similarity by Google

Congratulations to the Computer Vision Team at Google Labs! Google finally releases image search based on image similarities. 

Let’s review the different stages of image search:

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FashionLatte: Visual Search Engine for Clothes

FashionLatte is still brewing. FashionLatte is a clothes search engine based on images. It is “a one-stop shop for your daily cup of fashion”. The Co-Founders are three brilliant Ph.D. candidates from Computer Vision and Robotics Lab at the University of Illinois at Urbana-Champaign, i.e., Sanketh, Bernard, and Esther (that is how the company name SanBernest Inc. comes from). 
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Digg Uses Idee’s TinEye for Image Duplicate Checking

We introduced a great product of idee, TinEye, as one of the best visual search engines in a previous postDigg is a popular Web 2.0 application, where people can discover and upload content from anywhere on the web. These days I find Digg.com has partnered with idee’s TineEye for image duplicate checking, as shown in the following screen capture. 

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More iPhone Apps Using Computer Vision and Image Processing Technology

Since our previous post, Mobile Vision: iPhone Apps Employ Computer Vision and Image Processing Techs, more iPhone applications using computer vision and image processing technology have come out. Here we review two more apps.

  1. SnapTell: Tell the price, store, or online website of a CD/DVD/Book/Video Game with a photographed cover image. This is an image retrieval/recognition problem.
  2. CubeCheater: Solve your puzzle cube with six pictures taken for each face of the cube. It uses image segmentation and blob detection technology.

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Google and Microsoft Image Search by Content

Image search is going to an era of search-by-content (such as color, shape, texture, style, objects, etc) instead of search-by-text-context, as described in one of our previous posts, Visual Search Engines: The Future Seach Engines. Following Microsoft’s release of image search by content (color, style (illustration or photograph), or face) at the beginning of this month, Google today also announced their image search product by style (Clip art, Line drawings, and Photo content). I am happy to see more and more computer vision technologies are used in leading search engines.  

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Future iPhone Applications on Image Recognition

What are the future iPhone applications on computer vision?  Mac Funamizu, a graphic designer, came up with a set of new concept of using computer vision and image recognition techniques for Future Mobile Search. This is a perfect add-in to our previous post: Mobile Vision: iPhone Apps Employ Computer Vision and Image Processing Techs.

The future mobile search exploits advanced human computer interaction using build-in camera, fingers and touch screen, powerful capabilities of google search and google map, and the cutting-edge technologies in computer vision and image recognition, and brings engineers great inspiration and opportunities as well. 

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Best Visual Search Engines Review: (3) GazoPa

GazoPa is another image based visual serch engine (still in Beta). It is owned by Hitachi, Japan. Users can search similar images on the web based on image color, shape or faces. Similiar to Like.com, but GazoPa has interest of general objects rather than only commercial products. Different from TinEye, GazoPa does not focus on finding the same images.

Let’s first watch an overview video of GazoPa.

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Best Visual Search Engines Review: (2) Like.com


Like.com is one of the earliest visual search engines. It starts from another visual search engine called Riya, which was originally used to search celebrity people, objects in the pictures, and faces in the photos. From the profitability point of view, like.com now changes its business model to high-end online shopping based on visual search. 

The first thing I would like to talk about is like.com’s business model. There are always cool computer vision technologies. However, how to leverage the technology to make profit is a different thing. Like.com is undoubtedly a good model for visual technology entrepreneurs. Visual search is high-tech, and online shopping is something popular and something you can make profit from. The marriage of high-tech search engine with online shopping will undoubtedly bring you venture capital and eventually gain profit for you. 

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Best Visual Search Engines Review: (1) TinEye

TinEye

From now on, a series of posts will focus on reviewing several of the best visual search engines in the world. These search engines represents the state-of-the-art developments and applications in the field of visual search and retrieval. Each engine will be evaluated based on technology, functionalities, searching speed and accuracy, and business model.

The first one to be reviewed here is TinEye, owned by idee, a Canada based company. Tineye provides image search by image functionality, and show where and how the given image appears all over the web. This is one of the most brilliant applications of computer vision.

Let’s first watch an overview video for this technology.
Please enable Javascript and Flash to view this Blip.tv video.

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Visual Search Engines: The Future Search Engines

Text based search engines, such as Google, Yahoo, and Microsoft search engines, have become an important tool for us to obtain useful information through Internet. Interestingly, even you want to find a picture on the web, a text description of the picture has to be input to the search box. The search engine is then actually doing a text-context based search.

Imagine the following scenarios. Your girlfriend/wife came across a lady carrying a beautiful handbag on the 5th Ave of New York City. She quickly took a photo of that bag using her iPhone. Can she find similar bags instantly through iPhone? In another scenario when she came across a stylish Chanel handbag, which is too expensive for you to afford. Can you quickly find a cheaper one of similar style online?

Obviously, the current functionalities of major search engines are far from meeting our increasing demand. First of all, “Each picture is worth a thousand of words”, therefore, sometimes it is kind of difficult to describe what you really want using concise phrases. Secondly, even if you can describe the scene, many unrelated pictures are usually returned for you to manually sift what you need page by page.

Is there an efficient and automatic way to solve this problem? The answer is yes, I believe. Visual search will be the future and the ultimate solution for above scenarios.

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