WO2012052106A1 - Procédé de classification de motifs dans des blocs de données d'image - Google Patents

Procédé de classification de motifs dans des blocs de données d'image Download PDF

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Publication number
WO2012052106A1
WO2012052106A1 PCT/EP2011/004808 EP2011004808W WO2012052106A1 WO 2012052106 A1 WO2012052106 A1 WO 2012052106A1 EP 2011004808 W EP2011004808 W EP 2011004808W WO 2012052106 A1 WO2012052106 A1 WO 2012052106A1
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image data
statistical measures
cluster
clusters
sets
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PCT/EP2011/004808
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German (de)
English (en)
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Iris Paternoster-Bieker
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Iris Paternoster-Bieker
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Priority to EP11771014.5A priority Critical patent/EP2622540A1/fr
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/231—Hierarchical techniques, i.e. dividing or merging pattern sets so as to obtain a dendrogram

Definitions

  • the invention relates to a method for classifying patterns in image datasets, and to a computer program product.
  • Textures or patterns play an important role in the composition of natural images, their analysis and classification in various image analysis applications (Porter, R., Canagarajah, N .: Robust rotation invariant texture Classification: wavelet, Gabor filter and GMRF based schemes : IEE Proc.-Vis. Image Signal Processing 144 (1997), No. 3, pp. 180-188).
  • the field of application has a broad spectrum: surface inspection, texture object recognition, OCR, document segmentation, tissue recognition in medical images, automated visual examinations, content-based image search, and remote recognition (see Hiremath, PS, S .: Wavelet Based Features for Texture Classification In: GVIP Journal 6 (2006), No. 3, pp. 55-58).
  • CONFIRMATION COPY displayed local gray values. These values usually have constant or very little different properties within a texture. Thus, different textures can be recognized by comparing these values.
  • Structural models assume that the textures consist of texture primitives. They can be reproduced using primitive positioning controls (see Tuceryan, M., Jain, AK: The Handbook of Pattern Recognition and Computer Vision, World Scientific and Publishing Co., 1998. 207-248 p. //www.cs.iupui.edu/ ⁇ tuceryan/research/ComputerVision/texture- review.pdf).
  • Structural texture analysis models consist of two phases: a) Determining the texture elements b) Defining the positioning rules. This is applicable for very regular textures.
  • the texture primitives may be e.g. with edge detection using the Laplacian-of-Gaussian or difference-of-Gaussian method.
  • Wavelet theory has evolved over time as a unified basis for various signal processing applications such as multiresolution signal processing, machine vision, subband coding and speech compression (Chitre, Y., Dhawan, AP: M-band wavelet discrimination of In: Pattern Recognition 32 (1999), pp. 773-789)
  • multiresolution implies, it deals with the representation and analysis of a signal (image) in several resolutions. The charm of this method is that you can see the properties overlooked in one resolution in a different resolution.
  • the reason for a wavelet transform can be described as follows: When we look at an image, we see contiguous areas of similar textures and intensity levels that make up the objects. If these objects are very small in size and have low contrast, we examine them in higher resolutions. If they are very large and have high contrast values, they will be displayed coarser. But if there are both small and large objects or objects with low and high contrast in an image at the same time, it is advantageous to examine the image in different resolutions. This is the fundamental background for the "multi-resolution and Editing, see Gonzalez, RC; Woods, RE: Digital Image Processing. Pearson Education, Inc. Pearson Prentice Hall, NJ, 2008.
  • Discrete Wavelet function is the Haar wavelet, which is used in one embodiment of this invention.
  • the Haar wavelet was proposed by Alfred Haar in 1910 and is the first known wavelet in the literature. It is also referred to as the "D2" wavelet, a special form of "Daubechies wavelet".
  • the Haar wavelet is also known as a simple and easy orthonormal wavelet. This makes it very easy to implement.
  • the disadvantage is that the Haar wavelet is not continuous and therefore not differentiable. This property may be an advantage or disadvantage depending on the signal.
  • the discrete wavelet transform (wavelet series) of a function f (x) is defined by
  • the magnitude of the wavelet coefficients in a particular channel is greater for those images that have a strong texture property in orientation and frequency represented by the channel. Therefore, the texture of an image can be mapped with a feature vector formed by statistical measures of the wavelet coefficients from the respective channel (sub-band). This finally allows the use of the characteristics of the wavelet coefficients for texture classification.
  • the invention has the object to provide a method for the classification of patterns in image data sets, which can be used to improve conventional classification methods for texture classification.
  • a method for classifying patterns in image data sets, wherein the classification comprises fuzzy c-means clustering comprising ("training phase"): a) acquisition of training image data sets, wherein the training image data sets contain image data of different classes of B) wavelet transformation of the training image data sets to obtain a set of wavelet coefficients for each training image data set; c) determining a set of statistical measures for each training image data set from the associated set of wavelet coefficients; d) Classifying all sets of statistical measures of the training image datasets to form clusters of the sets of statistical measures, each cluster having a cluster center, each cluster center being associated with one or more of the classes of the patterns, e) determining the ambiguity associated clusters, which are several classes of Mu f) reclassifying the sets of statistical measures of the training image data sets of the unclassified clusters to form further clusters of the sets of statistical measures for each not uniquely associated clusters, each additional cluster having a further cluster center, each additional cluster center being associated with one or more of the classes of the patterns
  • Embodiments of the invention have the advantage that it is possible in a reliable manner to classify patterns in image data sets. If, in an initial classification pass according to steps a) -d), individual clusters are assigned to multiple classes of patterns for the purpose of determining cluster centers, the classification for these non-unique clusters is repeated in steps e) and f), thereby the recognition rate increases significantly.
  • the core of the invention is thus the cascaded repetition of steps e) and f) in combination of the determination of statistical measures from the coefficients of wavelet transformations and fuzzy c-means clustering. This ensures that sets of statistical measures of the training image records can be uniquely assigned to a single class of patterns.
  • This "training phase” thus creates a knowledge base (also known as "knowledgebase”) which can then be used to classify real test image data records.
  • cluster problems can be solved in general.
  • the problem with cluster problems is to group a set of data with similar properties and map them to the same cluster. Nearby data points have similar properties and are to be grouped into common clusters.
  • fuzzy C-Means each of the N data elements xi is not only assigned to a cluster, but to each cluster with a certain affiliation Pi j (x). Instead of minimizing the distance to the cluster center for all data elements, the distance of each element belonging to the cluster Center multiplied, cf. Chapter Fuzzy Logic, ln: Kramer, O .: Computational Intelligence, An Introduction, Springer Verlag, Berlin Heidelberg, 2009, pp. 75-99.
  • the cluster centers are calculated alternately according to the above equation in each step, and then the affiliations to the clusters are updated. These two steps are carried out alternately until the sum of the changes in the membership values ⁇ ⁇ falls below a value ⁇ . This defines the cluster centers and the knowledge base.
  • the knowledge base is thus initially built on the basis of a training data set.
  • the method further comprises the following steps (“test phase”): a) acquisition of test image data sets, b) wave-image transformation of the test image data sets to obtain
  • Step e) ensures that, even in the case of potential non-unique assignment of a set of statistical measures to one of the classes of patterns by the reclassification process, the corresponding set of statistical measures of the test image data set can be uniquely assigned to a single class of patterns.
  • step e) the clusters determined in step e) of the training phase are reclassified, using in each case the knowledge base of the individual 'cluster from step f) of the training phase.
  • the training image data sets image data include a first number of different classes of patterns, wherein the number of clusters formed by the classification of the training image data sets corresponds to the first number, wherein for a non-unique cluster - this cluster is assigned a second number of different classes of patterns,
  • the steps e) and f) of the training phase are repeated in cascaded fashion until the number of clearly assignable clusters has exceeded a predetermined minimum value.
  • the minimum value is at least 95%.
  • the statistical measures are chosen such that they are invariant with respect to rotation of the image data sets.
  • the method is stable executable, since the orientation of the image data sets no longer plays a role.
  • the method provides the same precise classification result. If rotation invariance is to be achieved, then the pairs of mutually diagonal channels from the wavelet transform are combined into one statistical feature (Porter, R., Canagarajah, N: Robust rotation invariant texture classification: wavelet, Gabor filter and GMRF based schemes : IEE Proc.-Vis. Image Signal Processing 144 (1997), No. 3, pp. 180-188)
  • wavelet transformations are otherwise scaling invariant, so that the scaling of the image data sets to be classified also plays no role.
  • the statistical measures include an energy value and / or an entropy value and / or a standard deviation.
  • ⁇ Energy In the analysis of the textures of wavelet-transformed images, the mean value of the magnitude of the wavelet coefficients is mostly used.
  • the energy of the nth channel is, according to Porter, R.; Canagarajah, N .: Robust rotation invariant texture Classification: wavelet, Gabor filters and GMRF based schemes.
  • IEE Proc.-Vis. Image Signal Processing 144 (1997), No. 3, pp. 180-188 is defined as follows:
  • i and j are the row and column of the nth channel, and x is the wavelet coefficient within that channel.
  • the third feature may be the entropy value in each channel. This feature has been used e.g. from Chitre, Y.; Dhawan, A.P .: M-band wavelet discrimination of natural textures. In: Pattern Recognition 32 (1999), pp. 773-789 describes and is calculated according to:
  • the training image records contain unique classes of patterns. In other words, a single image is not associated with 2 different classes, but the class assignment of the images is unique.
  • the method further comprises setting the set of statistical measures, wherein steps d) -f) are performed for different set of statistical measures defining the set of statistical measures for which the recognition rate of the training phase is maximized. This set of statistical measures thus determined is then also applied identically in the classification of the test images.
  • the determination of the set of statistical measures can be carried out, for example, as an intermediate step between steps c) and d). However, it can also be determined mathematically in advance (before step c) which sets of statistical measures should be used.
  • the invention relates to a computer program product having computer-executable instructions for performing the method steps as described above.
  • Fig. 2A part A of a tabular overview of the training phase under
  • Fig. 5 is a tabular overview of the wavelet error rate after the 2nd stage of the cascaded classification of the micrographs.
  • Figure 1 shows a graphical overview of various micrographs (publisher, steel iron: straightening series for the evaluation of the structure of annealed hot-work steels, SEP1614 board 2nd Verlag Stahleisen, Dusseldorf, 1996).
  • Micrographs of crude steel show microstructures that arise when the hot steel cools, and are a measure of the quality of the material.
  • the samples are taken from the annealed material, ground and polished and etched in 3% alcoholic nitric acid to reveal micro-homogeneity.
  • the sample viewed under microscope at a magnification of 500: 1 is assigned to an image of a guideline series.
  • FIG. 1 shows the straightening row with the steps from GA1 to GF5.
  • Figures 2A and 2B show a tabular overview of the training phase using 6 wavelet features:
  • the detection rate for pure wavelet features was 72.9%, 27.1% of the images (ie their sets of statistical measures) were incorrect Class assigned. Only 24 clusters were unique, 2 clusters contained 2 classes, 2 clusters consisted of 3 classes, a cluster of 4 and one of 5 classes.
  • cluster # 9 shows both a possible membership of class GC5 and class GD3, with 56 being assigned to sets of statistical measures (belonging to different images) in cluster # 9.
  • FIG. 3 shows the procedure in the diagram.
  • the images used for training purposes consist uniquely of k classes, ie that each image can be uniquely assigned to a single class.
  • These training image data are then classified according to steps b) through d) of the training phase, resulting in a number of k clusters of the sets of statistical measures of the training image data sets in step 302.
  • the training image data sets image data have a number k of different classes of patterns, the number of clusters formed by the classification of the training image data sets corresponding to the number k.
  • the knowledge base is updated accordingly.
  • the cluster centers of the sets are stored by a number p (p> 0) of statistical measures, each cluster center being uniquely assigned a class.
  • Each set of statistical measures can be graphically represented as a point in a p-dimensional space, with the associated cluster center also being arranged in this p-dimensional space. Any further set of statistical measures (for example, a test data set) that is near this cluster center can thus be identified as belonging to that cluster center.
  • step 304 to form further clusters of sets of statistical measures for each non-unique cluster in step 302, each additional cluster having a further cluster center, each additional cluster Center is assigned to one or more of the classes of patterns.
  • cluster 2 consists of n different classes
  • a classification is performed on n different classes.
  • clusters m consisting of I different classes
  • classification is performed on I different classes. This results in further clusters in step 306.
  • the knowledge base is updated accordingly. In accordance with the required recognition rate, this method can be continued cascaded for clusters that can not be assigned unambiguously until a desired recognition rate has been reached. This creates a new knowledge base for each cluster that is reclassified.
  • test execution is understood to mean the application of the method for classifying real images.
  • step 400 a collection of test image data sets is made, to which the classification knowledge base generated in FIG. 3 step 300 is applied.
  • a wavelet transformation of the test image data sets is first performed to obtain a set of wavelet coefficients for each test image data set, whereupon a determination of a set of statistical measures for each test image data set is made from the associated set of wavelet coefficients.
  • the cluster center mappings stored in the knowledge base generated in accordance with Figure 3 are used to classify all sets of statistical measures of the test image data sets to form clusters of the sets of statistical measures of the test image data sets. This results in step 402 different clusters 1..k.
  • the multiple assignment clusters known from training are reclassified, with the re-classification done using the previously determined other cluster centers. That is, for this purpose, the new knowledge bases generated in step 304 are used.
  • steps 404 clustering sets of statistical measures (i.e., individual test images) that can be uniquely assigned to corresponding classes.
  • FIG. 5 shows the non-unique clusters before and after the second classification. Of the previously 228 incorrect assignments, only 31 remain. This results in an improvement of the recognition rate from 72.9% to 96.3% for the cascaded classification of the training data from the wavelet features.
  • the o.g. Classified 1320 microstructures In a test phase, the o.g. Classified 1320 microstructures. In the first stage, 72.5% were correctly assigned, 362 images were wrongly classified. Subsequently, as shown in Figure 4, the multiple assignment clusters known from training were reclassified. In the second stage, another 321 images could be assigned correctly. The remaining 42 represented an error rate of 3.2% and a detection rate of 96.8%.
  • the method of a two-stage classification with fuzzy c-means clustering with the statistical features from the coefficients of wavelet transformation has the advantage that a very good classification result can be reliably achieved.

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Abstract

L'invention concerne un procédé de classification de motifs dans des blocs de données d'image. La classification comporte un regroupement « Fuzzy c-Means ». Le procédé comprend une détection de blocs de données d'image d'apprentissage qui contiennent des données d'image de différentes classes de motifs ; une transformation d'ondelettes des blocs de données d'image d'apprentissage afin d'obtenir un ensemble de coefficients d'ondelettes pour chaque bloc de données d'image d'apprentissage ; une détermination d'un ensemble de mesures statistiques pour chaque bloc de données d'image d'apprentissage faisant partie de l'ensemble associé de coefficient d'ondelettes ; une classification de tous les ensembles de mesures statistiques des blocs de données d'image d'apprentissage afin de former des regroupements des ensembles de mesures statistiques, chaque regroupement comportant un centre de regroupement qui, à son tour, est associé à une ou plusieurs classes de motifs ; une détermination des regroupements non significativement associés, qui sont associés à plusieurs classes de motifs ; ainsi qu'une nouvelle classification des ensembles de mesures statistiques des blocs de données d'image d'apprentissage des regroupements non significativement associés afin de former d'autres regroupements d'ensembles de mesures statistiques pour chaque regroupement non significativement associé, chaque autre regroupement comportant un autre centre de regroupement qui, à son tour, est associé à une ou plusieurs classes de motifs.
PCT/EP2011/004808 2010-09-29 2011-09-26 Procédé de classification de motifs dans des blocs de données d'image WO2012052106A1 (fr)

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CN107220977A (zh) * 2017-06-06 2017-09-29 合肥工业大学 基于模糊聚类的有效性指标的图像分割方法
CN107392952A (zh) * 2017-07-19 2017-11-24 天津大学 一种无参考混合失真图像质量评价方法

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CN104036300A (zh) * 2014-06-18 2014-09-10 西安电子科技大学 基于均值漂移分割的遥感图像目标识别方法
CN109255781B (zh) * 2018-09-03 2021-11-30 河海大学 一种面向对象的多光谱高分辨率遥感影像变化检测方法

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Cited By (6)

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Publication number Priority date Publication date Assignee Title
CN106845417A (zh) * 2017-01-20 2017-06-13 上海交通大学 基于特征池化与除归一化表示的高分辨率遥感图像分类方法
CN106845417B (zh) * 2017-01-20 2019-11-08 上海交通大学 基于特征池化与除归一化表示的高分辨率遥感图像分类方法
CN107220977A (zh) * 2017-06-06 2017-09-29 合肥工业大学 基于模糊聚类的有效性指标的图像分割方法
CN107220977B (zh) * 2017-06-06 2019-08-30 合肥工业大学 基于模糊聚类的有效性指标的图像分割方法
CN107392952A (zh) * 2017-07-19 2017-11-24 天津大学 一种无参考混合失真图像质量评价方法
CN107392952B (zh) * 2017-07-19 2019-12-06 天津大学 一种无参考混合失真图像质量评价方法

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