Google Books titles with color pictures – Botany, Medical Botany

[Examples below link to screenshots in Flickr of Overview : Selected Pages in GBS; links in Flickr go to actual GBS page.]

The Botanical Magazine, Or, Flower-garden Displayed
By William Curtis, vol 9, 1795, Harvard Univ

Curtis’s Botanical Magazine, Or, Flower-garden Displayed
By John Sims, vol 41, 1815, Harvard Univ

The Family Herbal
By John Hill, 1812, Oxford Univ

Flora Medica
By George Spratt, 1830, Oxford Univ

Vegetable Materia Medica of the United States, Or, Medical Botany
By William Paul Crillon Barton, 1818, Oxford Univ

Medicinal Plants (vol 2)
By Robert Bentley, Henry Trimen, David Blair, 1880, Harvard Univ

Medicinal Plants (vol 4)
By Robert Bentley, Henry Trimen, David Blair, 1880, Harvard Univ

Paxton’s Magazine of Botany, and Register of Flowering Plants (vol 1)
By Joseph Paxton, 1836, Oxford Univ

Strasburger’s Text-book of Botany
By Eduard Strasburger, Hans Fitting, Ludwig Jost, William Henry Lang, Heinrich Schenck, George Karsten, 1921, Univ California

Google Books titles with color pictures – Dermatology

Atlas and Epitome of Diseases of the Skin
By Franz Mraček, 1905, Stanford Univ

Atlas Der Hautkrankheiten, Mit Einschluss Der Wichtigsten Venerischen
By Eduard Jacobi, 1906, Stanford Univ

Atlas of Diseases of the Skin
By Franz Mraček, Henry Weightman Stelwagon, 1899, Stanford Univ

Illustrated Skin Diseases
By William Samuel Gottheil, 1902, Harvard Univ

An Introduction to Dermatology
By Norman Purvis Walker, 1906, Stanford Univ

On Diseases of the Skin
By Erasmus Wilson, 1865, Harvard Univ

Portfolio of Dermochromes (vol 2)
By Jerome Kingsbury, Eduard Jacobi, John James Pringle, William Gaynor States, 1913, Harvard Univ

Skin Diseases
By Melford Eugene Douglass, 1900, Univ Michigan

If you know of other areas that have books in Google Books with color pictures, please send comments.

A Flickr set has larger screenshots of these and four other countries.

The top image in each of the pairs below (”Full size image”) is a choice from the initial search in GSI (”blackbird” in the example below). Clicking “Similar images” for this choice goes to a set of refined images, represented by the bottom row of images in the pair. The blackbird example here shows some of the strengths and weaknesses of GSI. It often seems to do best with color photographs, but not so well with monocolor pictures. In the first instance, the red spot on the wing and the greenish background likely are clues used by GSI, to good effect. The lack of color clues in the second case is likely a problem for GSI. It also shows pretty clearly that GSI is getting clues from words associated with images, in this case causing it to confuse the blackbird with the US Air Force plane that has the same name.

The importance of color clues for GSI that’s shown in the example above occurs in several additional examples in the Flickr set — B/W line drawings especially cause problems for GSI. Here are some other observations from the Flickr examples:

• One notable example shows how GSI has a tendency to give too much weight to a word associated with a picture, as in the blackbird example — In a search for “george“, the “similar images” for a non-famous person named George are dominated by pictures of the recently prominent George Bush!
• GSI does best when the image is focused clearly on one subject; it doesn’t do well when there are multiple subjects, especially when they are unusual subject combinations, that don’t typically occur together.
• It does poorly with abstract or stylized, non-realistic images.
• Strongly featured text sometimes “pulls the attention” of GSI away from the “picture content” of the image.

Despite the problems described here, I think GSI is a true advance in the technology of image search. In general, it does a surprisingly good job of detecting similarity. So, kudos to Google engineers!

In large mass-production systems like Flickr, automatic thumbnails are unavoidable, and my point is not that they should never be used. Instead, my point is that, on many levels, pictures require more human input than text to make them optimally usable. Pattern recognition — the simple observation that the thumbnail of a picture of a sunset SHOULD CONTAIN THE SUN — is something that the human brain does easily, but this does not come naturally for a computer.

Another sort of problem in automatic production of thumbnails is making a thumbnail by simply reducing the size of the large picture. If the main subject of the picture is relatively small, it is not visible in a small thumbnail.

The picture to the left is from the Hardin Library ContentDM collection. The inset in the upper right shows the thumbnail that’s generated automatically by the system, which does a poor job of showing details of the picture. The lower inset shows a thumbnail made manually, which gives a much more clear view of the central image in the picture.

Cropping of a picture to produce a thumbnail, as done here, takes more subtle human judgement than the case with the Flickr picture in the first example, where the weakness of automatic production is obvious. With cropping, there’s inevitably a trade-off between showing the whole picture in the thumbnail or showing the most important subject of the picture. In cases such as this one from ContentDM, where most all of the detail in the picture will be lost in a small thumbnail, it seems better to focus on a central image that will show up in the thumbnail.

Finally, a few examples from Hardin MD, below, show how we have done cropping to improve the detail in our thumbnails. The thumbnails on the left in each of the three pairs are made by simply reducing the size of the full picture. On the right in each pair are the thumbnails we use, that we have made by cropping the full picture before making the thumbnail.

The biomedical, scientific pictures that we work with in Hardin MD are fairly easy to make thumbnails for, because they generally have a well-defined focus, that’s usually captured well by automatically-generated thumbnails. More artistic, humanities-oriented pictures, such as the ones discussed here from Flickr and ContentDM, however, often have more subtle subjects, that benefit from the human intelligent touch in the production of thumbnails.