Pinterest Vs. Gimmiebar

I’ve been on an inspiration curation kick lately. At the end of September, 2011, I signed up for Gimmiebar, a more niche-focused curation site akin to the web 2.0 social bookmarking site del.icio.us. The idea behind the service is when you stumble upon something awesome, be it an image, text, or most videos, you can save it to your gimmiebar using their handy browser extension or bookmarklet. You can find your friends and follow them like any social network and see what they add to their collections in the Discovery section. I must not be following the right people as now and then I will see what others are finding only to come up uninspired. There’s no sitewide search but there is a Notable section that you can peruse.

The gimmiebar extension for Chrome or bookmarklet for other browsers (both do the same thing) is slick. Activating your gimmiebar gives you two drop zones for dragging images into: your Public Firehose and Private Stash. Once you add your image you can add it to one or more collections or create a new collection on the fly. You’re given the option to give a description and tagging is done inline by adding a # before a word.

Gimmiebar does a really good job of being quick and painless. I also like how they save a copy of the image or website incase the original source should go offline. You can even hook up your Dropbox account and have your saved images saved to the cloud as another backup.

Overall Gimmiebar has good tools for personal curation but lacks in the social aspect. I’m sure this will get better over time as more and more people start using it.

Pinterest, on the other hand, has a huge focus on socially sharing interesting things found on the web. You create boards which you pin different things to. Your friends can see what you have pinned and even repin it to their boards. Repinning is just like reposting on Tumblr or re-tweeting on Twitter.

My favorite feature of Pinterest is their search feature, which works well. Just enter a term and you get back a large swath of different images to pore over. Since the community is so active, you will want to keep checking your favorite searches for new inspiration. Commenting is also there but I don’t see much conversation occurring on pins.

Pinterests audience is heavily female oriented. There is a lot of fashion, do-it-yourself crafts, wedding, recipes and decorating pins going through my stream. But that’s ok because there is also a lot of robots.

With all the buzz surrounding Pinterest and their traffic numbers going up and up every month, it’s no wonder the site can slow down to a crawl from time to time. Ingesting and searching through all of those images is a tough job for any systems engineer at Pinterest’s scale. Hopefully that $27 million in funding will ease some of their growing pains.

So while both invite-only services are based around the same concept, curating inspiration, I’ve found myself using both for different purposes. Gimmiebar is more for my design/photography/art inspiration while Pinterest is for collecting fashion and home ideas. Kristina is also on Pinterest and we share a board which is fun to pin stuff to when I find interesting stuff for her. It’s also neat to learn about your friends based on what things they collect.

Be sure to give both sites a try. I have plenty of invites for both. You can find me on Gimmiebar and on Pinterest.

Picasa People Tagging / Facial Detection Guide

Picasa 3.5 brings a new feature that scans your photo library looking for faces so you can tag people in your photos. This walkthrough video embedded below from Google covers the basics of people tagging. There is also this written guide.

First Impressions

The face detection technology built into Picasa 3.5 works ok. The scanning processes is slow but Google is aware of this problem. When it works, Picasa can group together common faces making tagging people a breeze. But when it doesn’t work you can get all kinds of wrong matches which are tedious to go through and correct. It’s certainly not something you can set and forget as you will need to spend some time double checking the suggested matches.

With that said there are a few tips and tricks to make the process a little easier. Most of the following info was culled from a help forum post.

Tips for better tagging

Set the suggestion and cluster threshold to 85. By default both of these values are set at 80. You can change this in the following locations:

Windows: Tools->Options->Name Tags
Mac: Picasa->Preferences->Name Tags

I found a noticeable decrease in false positives by bumping this setting up a notch, especially if your photo library is greater than about 10,000 photos.

Picasa Suggestion and Cluster Threshold

Be careful tagging blurry faced photos. When you have a bunch of blurry faces attributed to a person, the number of false positives goes way up as Picasa struggles to make a vague connection.

To see the unknown faces for only one folder at a time just do a search for the name of the folder and select it from the auto suggest list that drops down from the search box. Now you can easily go through the unnamed people for that folder alone cutting out the noise of unnamed people from other photos.

Picasa Filtering Unnamed People Using Search

When you’re combing through a bunch of faces, turn on the Faces filter at the top of the Picasa window. This will hide any photos that don’t have any faces in them saving you a bit of time when moving from picture to picture.

Picasa Show Only Photos with Faces button

How does Picasa’s facial recognition work?

Picasa scans the photo looking for facial patterns. When it finds a match, Picasa adds two pieces of information to a picasa.ini file (hidden by default) in the folder holding the picture. The face data is stored like this:

faces=rect64(907574589cc58a78),a30bebdb5c1a778d;

The first part, enclosed in rect64(…), is the relative coordinates for the rectangle around the face. The second set of characters after the comma is a unique identifier linking the face with a name in Picasa’s contact database which is stored in the following locations on your computer:

Windows: /Users/%USERNAME%/AppData/Local/Google/Picasa2/db3/
Mac: /Users/%USERNAME%/Library/Application Support/Google/Picasa3/db3/

(Source: Mye)

The 16 characters enclosed in rect64(…) is a 64-bit hexadecimal number which can be broken up into four 16-bit numbers used to identify the position of the rectangle used to mark the face. If you divide each of the four 16-bit numbers by the maximum unsigned 16-bit number (65535), you’ll get four numbers between 0 and 1 which give the relative coordinates for the face rectangle in the order: left, top, right, bottom.  To calculate the absolute coordinates, multiply the left and right relative coordinates by the width of the image and multiply the top and bottom relative coordinates by the height of the image.  This way the faces will always be identified even when the image is re-sized.

(Source: Oedious)

Embedding tagged people data in picture files

The fact that Picasa stores the tagged people data in an external data is less than ideal for some. The .NET program AvPicFaceXmpTagger reads Picasa 3.5 face definitions for a given list of pictures and writes them as XMP metadata tags inside the picture files. It can also add the person’s name as XMP keywords and/or IPTC keywords which can be read by other photo programs.

I haven’t tried it out myself but it is worth mentioning as a workaround until Google addresses this problem.

avpicfacexmptagger Main Picture Display

(Source: Andreas Vogel)

Conclusion

Overall the people tagging features introduced in Picasa 3.5 are a nice start but there is still a lot of work to be done. Hopefully the future improvements will be frequent and steady as this is an exciting new vector of information to make digging through photo collections a joy. Things will really get interesting when it will be able to talk to other photo services (like Facebook) to gather and sync and kinds of metadata.