Cadastral map interpretation
4.6 Experimental results
In this section, we evaluate the text/graphic segmentation and the quarter extrac-tion.
4.6.1 Quarter extraction Experiments 4.6.1.1 Methodology
The assessment phase aims to compare the contour obtained with our method and the contour described by a user. To illustrate our saying, we kindly refer the reader
4.6. Experimental results 109
(a) (b)
(c) (d)
Figure 4.49: (a) Extracted quarter ; (b) Polygon given by the Snake; (c) Polygon overlapped on the source image; (d) Isolated Quarter
to figure4.49. This evaluation is carried out by image difference between the user-defined ground truth (I2) and the computer-based contour built automatically (I1).
Image differences are depicted in figure4.50. The error is expressed in equation4.3.
Error= 1
P
x
P
yI2(x, y)
"
X
x
X
y
(|I1(x, y)−I2(x, y)|)
#
(4.3)
4.6.1.2 Evaluation
The error rate is computed on 30 ancient cadastral maps and results are reported in table 4.1. The errors can be categorized into two groups. (1)Quarter vectoriza-tion accuracy and (2)loss informavectoriza-tion from the retrieval system. Firstly, the active contour does not stick strictly (pixel to pixel) the input image. An accuracy error is introduced by the snake which proceeds to a polygonal approximation of the dig-ital curve. This first kind of error is somehow minor since it represents only 1%
of the whole mistake, in average over the 30 maps. Secondly, the biggest source of error is the missing information due to our selective method. Mainly, through the text/graphics separation stage some small CCs can be removed from the graphic layer while they represent part of the quarter structure. i.e.: edges or corners.
Nevertheless, we would like to precise that the error rate remains quite low.
(a) (b)
(c)
Figure 4.50: (a) Rasterized polygon contour given by the snake; (b) Ground-truth;
(c) Difference image
Min Mean Max Error (%) 1.58 4.91 12.80
Table 4.1: Error rates
4.7. Conclusion 111
Training data set Test data set Validation data set
]elements 4118 4118 5000
]text 2791 2791 2500
]graphic 1327 1327 2500
Table 4.2: Databases in use for the text/graphics segmentation
Precision Recall CCI Rec
Class1 Class2 Class1 Class2 Class1 Class2 (%)
K=1 0.838 0.783 0.764 0.852 1910 2130 80.8
K=10 0.875 0.785 0.756 0.892 1884 2236 82.4
K=100 0.856 0.898 0.904 0.848 2122 2258 87.6
Table 4.3: Classification rate. Class1 and class2 represent graphics and text respec-tively.
4.6.2 Test on Text/Graphic segmentation
Methodology The text graphic segmentation is assessed according to the number of correctly classified CCs as text or graphic. In this objective, three databases were employed and are described in table4.2, this latter shows the data set characteristics.
The training and test sets are involved during the training phase by the genetic algorithm while the validation database is only used once to assess the whole system.
Results
Table 4.3takes the stock of the recognition rates (Rec) on the validation database.
Results deal with the need to generate prototypes instead of just finding them among the graph corpus. Moreover, increasing the number of generated prototypes helps to improve the number of correctly classified instances (CCI). Respectively, Class1 and Class2 stand for Graphics and Text. The class variabilities are better taken into account as the number of prototypes increases. The problem is better modeled with generalized prototypes which maximize the classification rate.
Figure 4.51deals with a comparison between the well-known Fletcher and Kas-turi method and our approach on a cadastral map. Of course, this is a single and unique image, but anyway, it reflects the behavior of the two paradigms. Our ap-proach is more complex however it gives a better representation of the text layer.
In addition, a comparative study is reported in table4.4. It presents a quantitative assessment.
4.7 Conclusion
This chapter discussed four important phases.
• Firstly, a text/graphics separation was proposed. It is based on a graph
rep-#Graphics #Text #Elements CCI Rec Class1 Class2
Fletcher-Kasturi 763 122 855 435 57 55.59
Table 4.4: Comparative Study
(a) (b)
Figure 4.51: (a) The text layer given by the Fletcher and Kasturi approach; (b) the text layer found by our method.
resentation and a prototype selection method for structural data. A classifier trained on these prototypes categorizes connected components into two broad groups, text or graphic.
• Next, the quarter localization is carried out through a coarse to fine approach.
The architecture is based on a “peeling the onion” strategy in order to remove unwanted objects from the map. This aspect was assessed on 30 maps and results tend to illustrate a reliable behavior.
• Thirdly, the parcel extraction is performeda posteriori. A closer look is given to parcels when quarters are individually identified. Specific image processing are carried out to delineate parcel borders. Hereafter, the joint use of a polyg-onal approximation method and a cycle detection transform pixels in segments and segments in polygons, respectively. The question of parcel accuracy will be highlighted in the next chapter, dealing on performance evaluation.
• In the fourth stage, a knowledge integration strategy is proposed for veri-fying image processing integrity. In this objective, a data driven ontology generator has been defined. Data are generated from object detectors to form an instance model where relations and concepts are specified into an expert-designed ontology. This structure stands apart from a conventional graph-based representation (a "vanilla plain" graph) because concepts and relations that compose the graph are explicitly written into a knowledge base called on-tology. Thereafter, this computer-generated model comes to feed a higher level of representation. A meta-model is automatically inferred from an instance model making possible the comparison with an expert-designed knowledge
4.7. Conclusion 113
representation. This comparison is led thanks to a graph matching algorithm that provides quantitative measures about how different are two meta-models.
Furthermore, another information is retrieved by the graph matching method, in this way, it gives out the nodes where errors occur into the graphs.
From this last remark an important issue arises. Object detectors and nodes are closely coupled, therefore, it is possible to spot which low level process is corrupted and does not generate the compliant information. These indications make possible a feed back on low level processes changing and adjusting their parameters (threshold) to obtain iteratively a "better" meta-model conforms to the expert knowledge.
Ongoing works are investigating the possibility to correct errors through the use of perception cycle while another future work is exploring an orthogonal direction, the use of ontology reasoners to automatically label nodes of a model graph. In this situation, relations between nodes could be considered as constraints that must be fulfilled to classify objects into broad categories.