Description of the method

The project’s framework that includes graphinder’s operation determines a fluent bilateral circulation of graphic data. This circulation compares two image models which, in order to achieve the goals proposed for this research, will ultimately maintain certain similarity in most of their component physical attributes. Or, in other words, the said development shows a similarity of iconographic patterns obtained as a result of comparing the graphic information, and this confirms that the data exchange found a series of values related to both images.

Therefore, we will start by briefly defining these two iconographic typologies, in order to get used to their personal mission in the devised interface.

1. INPUT Image (or Pattern)

A complete image or part of it that, in its digital state, will be employed as an initial search standard in the extensive catalogue of graphic data supplied by the Internet. Explicit preliminary reference that articulates the exploration.

2. STABLE Image (or Exposed Image)

This name refers to images with any graphical format – included in the bitmap catalogue: GIF, JPG, BMP, TGA, TIFF, etc. - and from many different genders (painting, photography, illustration, etc.) previously inserted in the web pages that are part of the web’s volume of information.

...

When we carry out a search using text, we resort to values determined by the chains of characters that make up the linguistic term or terms; in the iconographic search, we will set the attributes for each of the pixels concentrated in a graphic area of the image as the cataloging clause or archetype. Thus, the qualities attributed to color, which can be quantified using numbers, will be the main initial parameter needed to specify accurately the numerical code that equals or differentiates one dot matrix from another. This deduction will be analyzed in the following chapter.

Top

Physical characteristics of the pixel matrix

Size of the matrix

Albrecht Durer, in his famous treatise on measurement Underweysung der Messung, divided the space where the drawing was to be made using a proportional grid (illustration 2). The result of the experiment was a gridded surface in the shape of a window that contained imaginary coordinate axes, which would make easier to locate the most interesting points projected on the model's features while he/she posed behind the "window". The pixel matrix extension that we will have in our graphic search interface is very similar to this Renaissance tool; the definitive practice stipulated is based on the particular distribution and attributes of each one of these tiny pixels taken as a whole.

Illustration 2
Artist painting a lying model. Albrecht Durer

Therefore, one of the first tasks to be performed is defining the size (height and width) of the matrix that will receive the input images. Considering that this standard size will be equally appropriate for any iconography used, and that it will be sufficient to solve the calculations generated by the operation of searching its homonymous image or images, we will agree on a number of pixels specifying a minimum and a maximum. Below the minimum, it is understood that the image would be very difficult to read by the search systems established, or it could even be mistaken with other stable images with a similar chromatic range.

Another possibility is using a TESSELATION method, i.e. segmenting the real graphic area of the image in a number of subdivisions. This way, we will cover and analyze an important part of the image, obtaining a greater reliability of results when comparing the data. Each one of these image fractions will be first independently investigated, and then globally analyzed with the rest of the fragments provided.

As shown in illustration 3, if we insert -as input- a fragment of the image to be located, this fragment should be sufficiently significant when compared with the image as a whole to permit the best search possible through the web’s conduits without suffering the aforementioned color mismatching. For example, if we have an image with large surfaces in different shades of plain colors, it is convenient to discard these areas as a search pattern, given the deficient quantity and quality of graphic information they supply because of their chromatic homogeneity. In the same illustration by Raphael, the matrix that contains the shape of the image isolated as input has an area of 55 x 63 pixels or dots (height/width). This would be and adequate graphic fragment to start an efficient web search, because it has the appropriate size and because it has been extracted from an area with suitable descriptive characteristics. It could be part of a greater variety of tiles adjacent to the first one or scattered around the orography of the image.

Illustration 3
Pixel matrix (Virgin at the Rose, by Raphael). Manuel Viñas Limonchi

Chromatic composition of the pixels

As we already know, in the digital domain the treatment of color follows a series of codes perfectly organized into ranges or models, which create groups with some of these colors. Regulated by the CIE (Commission Internationale de l'Eclairage), there are models – sometimes organized as color palettes - such as RGB (which will be explained later on), CMYK (especially used for color printing, and composed of cyan, magenta, yellow and black) or HSB (hue –H, saturation –S, and brightness –B, which determine, in this order and using numerical characters, the color, its depth and intensity). One more could be added; the PANTONE (PANTONE^© Formula Guide, a product of Pantone, Inc.) is used -as the CMYK- in printing applications for the graphic arts industry. Its colors are described in a schematic guide or table by means of alphanumerical codes, in order to help choosing the right ink. For example, PANTONE^© 7487 C would have a chromatic correspondence with light green.

Out of the models mentioned above - and some more -, we prefer the RGB, as it is the one employed by digitalization devices such as the scanner, by display devices such as the screen and, beyond technological considerations, we could consider this model as the one that regulates our binocular vision. For this reason, given the importance of the digitalization peripherals as mediating instruments between the original image – possibly with an analogue or traditional manufacture - and the image that we will use as input, the said RGB model becomes the most suitable language for interpreting or translating the graphic data, thanks to its color qualities.

Deciphered into numbers, each one of the three channels that make up this model of additive colors (red, green and blue) has been assigned a value from 0 to 255. For example, the numeric expression “0,0,255” would correspond to a deep blue with no mixture of red or green. The numbers “0,0,0” would give black, and “255,255,255”, would give white. Following this schematic procedure, we obtain a matrix in which each one of its cells provides three values that refer to that particular pixel.

Illustration 4
Chromatic values of the pixel according to HSB and RGB models. Manuel Viñas Limonchi

Top