The design of a content-based retrieval system depends on requirements driven by users. However, in some cases, what machines are capable of doing does not fully conform to these needs. This section briefly explains the main issues in designing a simple system capable of performing content-based analysis of craquelure patterns. Obstacles and implementa- tion issues can be drawn out from the computer vision point of view and also from the requirements of potential users.
2.6.1 Detection Accuracy
From the computer vision point of view, the process of identifying crack patterns is believed to be a very critical step. The relative difficulty in detecting cracks depends on whether their shape and typical orientation are knowna priori, whether they start from the edge of the object, and whether the texture is periodic or random. A key problem is the typically very small transverse dimensions and poor contrast of cracks. The human visual system may easily detect them, but they may actually consist of “chains” of non-adjacent single pixels in the image. In some of the worst cases, the surface is highly textured (with brush stroke patterns) and this will certainly pose a problem for the detection stage. However, details may not be too important since in classifying craquelure patterns the key features to detect are the dominant ones.
2.6.2 Feature Selection
Feature selection is very important, since good pattern discrimination can only be achieved if highly distinguishable features are used. Selecting relevant features is not an easy task, firstly since there is no clear grammar or language that can explain precisely how a particular crack pattern differs from another. The scenario certainly does not mimic that of the optical character recognition (OCR) problem [74, 75, 76, 77] where each alphabetical/numerical character has unique structural features allowing relatively easy classification, assuming ideal block-based characters. This is not the case for cracks, since the perception towards a particular pattern varies with respect to the observer and the scope of view (shape and scale).
2.6.3 Objects-of-interest
Content-based processing does not assume everything within an image to belong to the same object-of-interest. An image contains objects with different shapes, sizes and appearance.
Taking an easy example, a car is a parent object to sub-objects such as the wheels, doors and body. Each of these objects is distinguishable by their colour, texture or shape. Looking at a slightly more complicated scenario, a human face can be further segmented into several obvious sub-objects, namely the ears, nose, eyes and mouth. These sub-objects are quite similar in colour but differ in terms of their shape and also position on the face.
The scenario is much more challenging when attempting to segment crack patterns which, to begin with, do not contain any colour information, possess similar texture properties and offer no prior knowledge of any positional attributes. The only feature that can be used to extract the objects-of-interest is the shape of the cracks and how these structures combine.
Another observed complication stems from the issue of subjective perception, where crack patterns change with varying viewpoints, i.e. the scope of view. General observers might have different opinions about the likelihood of the objects-of-interest.
Figure 2.2: Subjective perception regarding crack pattern change with different view-
points.
This is seen as a very big challenge. As shown in Figure2.2, the view in which crack patterns are observed affects the perceptive notion as to how they are classified. Furthermore, crack patterns consist of multiple line segments either connected or separated.
2.6.4 Hard versus Fuzzy Class Assignment
Another computer vision related issue concerns the classification of crack patterns. As mentioned earlier, there is no existing standard grammar or language to describe crack patterns. It is not quite true to say that a certain crack pattern exclusively belongs to a particular class. Unless a crack pattern totally agrees to a particular class descriptor, while contradicting to descriptors of the classes, it can be assumed that each crack pattern is a member of every crack class but with a varying confidence value. Experiments are conducted in the later stages of the dissertation to show the significance of the claim.
2.6.5 Retrieval Efficiency
To be able to serve potential users with retrieval functionalities, the system should be able to process and access data in a considerably short time. Hence speed is a highly desirable design element, which can directly result from efficient handling of data queries and quick database look-up. Museum image collections which include X-ray and visible images are very large in size (some exceeding 10000x10000 in pixel resolution), thus requiring efficient algorithm executions. The system’s approach towards the implementation of content-based functionalities also poses some questions, one of these being the type of query and whether or not it is useful for the end-users, bearing in mind that such applications might only be useful for fine artwork conservators. Relevant query types in this context are query by example and query by type (text-based query). Examples of these queries are further elaborated in Section 2.7.