| 1. |
- Gelzinis, Adas, et al.
(författare)
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Quality function for unsupervised classification and its use in graphic arts
- 1999
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Ingår i: Journal of Advanced Computational Intelligence. - Japan. ; 3:6, s. 532-540
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we propose quality function for an unsupervised neural classification. The function is based on the third order polynomials. The objective of the quality function is to find a place of the input space sparse in data points. By maximising the quality function, we find decision boundary between data clusters instead of centres of the clusters. The shape and place of the decision boundary are rather insensitive to the magnitude of the weight vector established during the maximisation process. A superiority of the proposed quality function over other similar functions as well as conventional clustering algorithms tested has been observed in the experiments. The proposed quality function has been successfully used for colour image segmentation.
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| 2. |
- Verikas, Antanas, 1951-, et al.
(författare)
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A New method for colour measurements in graphic arts
- 1999
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Ingår i: Color Research and Application. - New York : Wiley-Blackwell. - 0361-2317 .- 1520-6378. ; 24:3, s. 185-196
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Tidskriftsartikel (refereegranskat)abstract
- This article presents a method for colour measurements directly on printed half-tone multicoloured pictures. The article introduces the concept of colour impression. By this concept we mean the CMY or CMYK vector (colour vector), which lives in the three- or four-dimensional space of printing inks. Two factors contribute to values of the vector components, namely, the percentage of the area covered by cyan, magenta, yellow, and black inks (tonal values) and ink densities. The colour vector expresses integrated information about the tonal values and ink densities. Values of the colour vector components increase if tonal values or ink densities rise and vice versa. If, for some primary colour, the ink density and tonal value do not change, the corresponding component of the colour vector remains constant. If some reference values of the colour vector components are set from a preprint, then, after an appropriate calibration, the colour vector directly shows how much the operator needs to raise or lower the cyan, magenta, yellow, and black ink densities in order to correct colours of the picture being measured. The values of the components are obtained by registering the RGB image from the measuring area and then transforming the set of registered RGB values to the triplet or quadruple of CMY or CMYK values, respectively. Algorithms based on artificial neural networks are used for performing the transformation. During the experimental investigations, we have found a good correlation between components of the colour vector and ink densities.
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| 3. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Colour classification by neural networks in graphic arts
- 1998
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Ingår i: Neural Computing & Applications. - London : Springer London. - 0941-0643 .- 1433-3058. ; 7:1, s. 52-64
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a hierarchical modular neural network for colour classification in graphic arts, capable of distinguishing among very Similar colour classes. The network performs analysis in a rough to fine fashion, and is able to achieve a high average classification speed and a low classification error. In the rough stage of the analysis, clusters of highly overlapping colour classes are detected Discrimination between such colour classes is performed in the next stage by using additional colour information from the surroundings of the pixel being classified. Committees of networks make decisions in the next stage. Outputs of members of the committees are adaptively fused through the BADD defuzzification strategy or the discrete Choquet fuzzy integral. The structure of the network is automatically established during the training process. Experimental investigations show the capability of the network to distinguish among very similar colour classes that can occur in multicoloured printed pictures. The classification accuracy obtained is sufficient for the network to be used for inspecting the quality of multicoloured prints.
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| 4. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Colour image segmentation by modular neural network
- 1997
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Ingår i: Pattern Recognition Letters. - Amsterdam : Elsevier. - 0167-8655 .- 1872-7344. ; 18:2, s. 173-185
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Tidskriftsartikel (refereegranskat)abstract
- In this paper segmentation of colour images is treated as a problem of classification of colour pixels. A hierarchical modular neural network for classification of colour pixels is presented. The network combines different learning techniques, performs analysis in a rough to fine fashion and enables to obtain a high average classification speed and a low classification error. Experimentally, we have shown that the network is capable of distinguishing among the nine colour classes that occur in an image. A correct classification rate of about 98% has been obtained even for two very similar black colours.
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| 5. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Increasing colour image segmentation accuracy by means of fuzzy post-processing
- 1995
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Ingår i: 1995 IEEE International Conference on Neural Networks. - Piscataway, NJ : IEEE Press. - 0780327691 ; , s. 1713-1718
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Konferensbidrag (refereegranskat)abstract
- This paper presents a colour image segmentation method which attains a high segmentation accuracy even when regions of the image that have to be separated are very similar in colour. The proposed method classifies pixels into colour classes. Competitive learning with `conscience' is used to learn reference patterns for the different colour classes. A nearest neighbour classification rule followed by a block of fuzzy post-processing attains a high classification accuracy even for very similar colour classes. A correct classification rate of 97.8% has been achieved when classifying two very similar black colours, namely, the black printed with a black ink and the black printed with a mixture of cyan, magenta and yellow inks.
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| 6. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Soft combination of neural classifiers : a comparative study
- 1999
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Ingår i: Pattern Recognition Letters. - Amsterdam : Elsevier. - 0167-8655 .- 1872-7344. ; 20:4, s. 429-444
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents four schemes for soft fusion of the outputs of multiple classifiers. In the first three approaches, the weights assigned to the classifiers or groups of them are data dependent. The first approach involves the calculation of fuzzy integrals. The second scheme performs weighted averaging with data-dependent weights. The third approach performs linear combination of the outputs of classifiers via the BADD defuzzification strategy. In the last scheme, the outputs of multiple classifiers are combined using Zimmermann's compensatory operator. An empirical evaluation using widely accessible data sets substantiates the validity of the approaches with data-dependent weights, compared to various existing combination schemes of multiple classifiers.
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| 7. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Soft fusion of neural classifiers
- 1998
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Ingår i: ICONIP'98. - Burke, VA : IOS Press. - 4274902595 ; , s. 195-198
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Konferensbidrag (refereegranskat)abstract
- This paper presents three schemes for soft fusion of outputs of multiple neural classifiers. The weights assigned to classifiers or groups of them are data dependent. The first scheme performs linear combination of outputs of classifiers and, in fact, is the BADD defuzzification strategy. The second approach involves calculation of fuzzy integrals. The last scheme performs weighted averaging with data dependent weights. An empirical evaluation using widely accessible data sets substantiates the validity of the approaches with data dependent weights compared to various existing combination schemes of multiple classifiers. The majority rule, combination by averaging, the weighted averaging, the Borda count, and the fuzzy integral have been used for the comparison.
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| 8. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Using Labelled and Unlabelled Data to Train a Multilayer Perceptron for Colour Classification in Graphic Arts
- 1999
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Ingår i: Multiple approaches to intelligent systems. - Berlin : Springer Berlin/Heidelberg. - 9783540660767 - 9783540487654 ; , s. 550-559
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Konferensbidrag (refereegranskat)abstract
- This paper presents an approach to using both labelled and unlabelled data to train a multi-layer perceptron. The unlabelled data are iteratively pre-processed by a perceptron being trained to obtain the soft class label estimates. It is demonstrated that substantial gains in classification performance may be achieved from the use of the approach when the labelled data do not adequately represent the entire class distributions. The experimental investigations performed have shown that the approach proposed may be successfully used to train networks for colour classification in graphic arts.
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