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Special issue on “Innovative knowledge based techniques in pattern recognition”
Pattern Recognition Letters 34 (2013) 1567–1568
Contents lists available at SciVerse ScienceDirect
Pattern Recognition Letters journal homepage: www.elsevier.com/locate/patrec
Editorial
Special issue on ‘‘Innovative knowledge based techniques in pattern recognition’’ The setting of the special issue was an ambitious proposal that has been answered enthusiastically by the pattern recognition community. Though initially planned for the attendance to the 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, KES 2012, hosted in San Sebastian, Spain in September 2012, it has been opened to a broader community, resulting in many contributions, so that almost two thirds of the accepted papers come from the general call, reflecting the interest of the pattern recognition community towards the topics faced in this special issue. Aim of the special issue is to find new ways to deal with the pattern recognition problem in data domains that require specific representations and operators. The resulting collection of papers reflects a wide diversity of approaches and problem domains, some of them radically innovative, others novel elaborations of already established techniques. Overall, the special issue offers a view of an actual wave of innovation in pattern recognition. Regarding computational methods, some innovative propositions are found in the special issue, with a special mention to some instances of the emerging Lattice Computing paradigm. On the application side, the range goes from robotic applications to environment discovery and semantic grounding to medical and remote sensing image processing, including some financial data processing. Hence, the special issue covers many of the hot topics in pattern recognition literature today. The introductory presentation of the papers follows: The paper by Wanatabe proposes a highly automatic selforganizing pattern recognition system based on the notion of compressibility, and on similarities obtained from the textization of the images and the dictionaries obtained from the application of lossless compression algorithms. This approach is a radical departure from the predominant paradigm of statistical pattern recognition, which opens a broad avenue for research. The paper by Ramík et al. proposes a new curiosity based approach to bridge the gap between the low level description of the image data and the high level of the semantic description of the image content. Curiosity is the driving force behind exploration, and the interaction with a human tutor provides the semantic grounding of the perceptual information stream. The approach is demonstrated on a humanoid robot exploring its surroundings. The paper by Valle et al. is an instance of the recently identified Lattice Computing field, where the basic computational constructs are lattice operators. The construction of knowledge representations by auto-associative memories with such kinds of computational premises has lead to a host of innovative approaches for image processing. Specifically, the quantale based auto-associative memories is able to deal with vector valued images, i.e. RGB color images, overcoming the problem of defining appropriate lattice operators over such images.
0167-8655/$ - see front matter Ó 2013 Published by Elsevier B.V. http://dx.doi.org/10.1016/j.patrec.2013.06.006
The paper by Chyzhyk et al. is another instance of Lattice Computing approaches. The innovative computational construction is an ensemble of Dendritic Computing classifiers, trained by bootstrapping and fused by majority voting. This approach is applied in an active learning paradigm to the segmentation of CTA medical images of Abdominal Aortic Aneurysm, with the aim of providing an easy to use and quick interactive tool for the segmentation of these large data volumes. The paper of Papakostas et al. reveals a close relation between Lattice Computing and Fuzzy Systems modeling. The paper proposes an exhaustive and formalized review of intuitionistic fuzzy sets, with a detailed explanation of distance measures and an experimental illustration on benchmarking data of the relative advantages of each proposed distance. The paper by Burduk proposes an interval valued weighting of classifier results for their fusion regarding ensembles of classifiers. The advantage of this approach is that a measure of uncertainty is provided for each individual classifier and propagated to the final result. Approaches based on accuracy and error, dual views of the problem, are developed and tested on well known benchmark data. The paper by Bennasar et al. deals with an important aspect of knowledge representation for classification and reasoning, that of feature selection. The proposed approach looks for the greedy maximization of the interaction between features selected and candidates. The approach avoids combinatorial complexity explosion effectively and provides state of the art results. The paper by Ouzounisa et al. reports quality assessment results on an ongoing big data project for the determination of a global map of human settlements from multimodal remote sensing data. This is an excellent example of a real life application requiring the processing of large quantities of data with all kinds of data quality issues. The computational approach includes innovative parameter-less mathematical morphology and texture high resolution image analysis of multiple image modalities. The final classification layer is provided by statistical learning, offering an instance of innovative and real-life working combination of pattern recognition and image processing techniques. The paper by Priego et al. offers another example of remote sensing high dimensional image data processing. The main emphasis being in the proposal of a technique, which is inherently suitable for parallel implementation and extraordinary computational speed-ups, based on the classical cellular automata. The application of tailored evolution algorithms allows to domesticate the complexity of training such kind of systems for real life high dimensional image segmentation. The paper by Veganzones et al. deals with the construction of relevance feedback mechanisms to tailor the content based search
1568
Editorial / Pattern Recognition Letters 34 (2013) 1567–1568
in image databases to the user needs and requirements. The approach is based on the computation of dissimilarity spaces obtained from a two class classifier trained on the features given as the distances of the image to a set of prototypical images, that are iteratively selected from the database to narrow the search. Most informative prototypes are given by the uncertainty of the classification. The paper by Roy et al. introduces a novel knowledge based biclustering method for gene expression data, a hot topic in pattern recognition applications in the bioinformatics area. The single-pass property of the approach is rather enticing for the high computational demands imposed by this kind of application. Current growth of gene expression studies as a routine analysis for many research endeavors ask for such quick and accurate procedures. The paper by Sheen et al. goes into the real of biocultural inspiration for the development of innovative computational process, including pattern recognition demonstrated on the malware detection. The computational process is the musical harmony inspired optimization of parallel ensembles of classifiers. The approach effectively balances computational cost and accuracy, achieving state of the art results. The paper by Tian et al. proposes an innovative cascade classifier, combining Haar and shapelet features to test diverse conditions in pedestrian image sequences, specifically cascades of Haar feature based classifiers remove effectively background features, while the shapelet based classifiers are aimed to the detection of pedestrian shape features. The paper by Favorskaya et al. provides a rich combination of image processing and machine learning process for the reconstruction of background data in image sequences. The temporal and spatial processing involves the texture segmentation of the image sequence, the estimation of smoothness, structure and isotropy by bioinspired classifiers and the texton based image reconstruction. The paper by Sarlin is an exciting new approach to the analysis of the current economical and financial turmoil from an already established brand of pattern analysis tools, the famous Self-Organizing Map, upgraded with time series analysis similarity abilities. The approach is able to identify temporal structures signaling major turning points of our current and ongoing crisis, searching for solutions. The paper of Tedin et al. proposes an knowledge based innovation of the classical active shape segmentation by modeling the appearance of image contours by an innovative classification method, with exciting applications in human computer interfaces based on gestures. The paper by Maraval et al. proposes the fusion of learning automata and probabilistic knowledge based method for classification of images, including formal convergence proofs. The salient aspect of this contribution is its ability to learn continuously by
adapting the probabilistic representation while the stream of data is being received. The data granulation allows the flexibility of the object representation. The whole approach is applied to holistic image recognition for landmark based navigation. The paper by Ortiz et al. proposes another fusion of well known methods for the computed aided diagnosis of Alzheimer’s Disease, which is a high priority social and scientific challenge. The careful processing of the high dimensional data given in the 3D MRI volumes sets a methodological standard for such studies, which are contributing to the collective effort of finding non-invasive image biomarkers of the disease, allowing early diagnostic and improved life quality. The paper by Vilamala et al. addresses another very dramatic problem in medical information processing: the identification of brain tumor type by Magnetic Resonance Spectroscopy data analysis. Such data is analyzed by finding an adequate decomposition into the constituent sources. Addition of a priori knowledge to the conventional non-negative matrix factorization boots the performance of the process and subsequent tumor identification. The paper by Filipczuk et al. treats another kind of cancer, i.e. breast cancer, from the point of view of image classification of cytological samples. After an adaptive feature extraction process extracting the cell nucleus, a suite of random support classifiers is applied and the result is input to a trained classifier fuser. The approach provides an efficient and accurate computer based assistant to the human cytologist actually doing the diagnosis. The paper by Sanchez et al. proposes a comprehensive framework for computer assisted clinical decision systems. The system encompasses several information sources ranging from the medical literature to the clinical evaluations, analytical results and image biomarkers. The system aims to be minimally disruptive of the clinical decision making practice, therefore its output is given as a selection of reasoned recommendations with a support value. The system aims to be subjected to continuous learning adapted to the evolving clinical paradigm. We thank specially Dr. Gabriella Sanniti di Baja for her support and close follow up of the special issue editorial management. Manuel Graña Basque Country University UPV/EHU, Department of Computer Science and Artificial Intelligence, Spain Michal Wozniak Wroclaw University of Technology, Department of Systems and Computer Networks, Poland Nima Hatami University of California San Diego, Imaging Data Evaluation and Analysis (IDEA) Center, USA Available online 14 June 2013
Contents lists available at SciVerse ScienceDirect
Pattern Recognition Letters journal homepage: www.elsevier.com/locate/patrec
Editorial
Special issue on ‘‘Innovative knowledge based techniques in pattern recognition’’ The setting of the special issue was an ambitious proposal that has been answered enthusiastically by the pattern recognition community. Though initially planned for the attendance to the 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, KES 2012, hosted in San Sebastian, Spain in September 2012, it has been opened to a broader community, resulting in many contributions, so that almost two thirds of the accepted papers come from the general call, reflecting the interest of the pattern recognition community towards the topics faced in this special issue. Aim of the special issue is to find new ways to deal with the pattern recognition problem in data domains that require specific representations and operators. The resulting collection of papers reflects a wide diversity of approaches and problem domains, some of them radically innovative, others novel elaborations of already established techniques. Overall, the special issue offers a view of an actual wave of innovation in pattern recognition. Regarding computational methods, some innovative propositions are found in the special issue, with a special mention to some instances of the emerging Lattice Computing paradigm. On the application side, the range goes from robotic applications to environment discovery and semantic grounding to medical and remote sensing image processing, including some financial data processing. Hence, the special issue covers many of the hot topics in pattern recognition literature today. The introductory presentation of the papers follows: The paper by Wanatabe proposes a highly automatic selforganizing pattern recognition system based on the notion of compressibility, and on similarities obtained from the textization of the images and the dictionaries obtained from the application of lossless compression algorithms. This approach is a radical departure from the predominant paradigm of statistical pattern recognition, which opens a broad avenue for research. The paper by Ramík et al. proposes a new curiosity based approach to bridge the gap between the low level description of the image data and the high level of the semantic description of the image content. Curiosity is the driving force behind exploration, and the interaction with a human tutor provides the semantic grounding of the perceptual information stream. The approach is demonstrated on a humanoid robot exploring its surroundings. The paper by Valle et al. is an instance of the recently identified Lattice Computing field, where the basic computational constructs are lattice operators. The construction of knowledge representations by auto-associative memories with such kinds of computational premises has lead to a host of innovative approaches for image processing. Specifically, the quantale based auto-associative memories is able to deal with vector valued images, i.e. RGB color images, overcoming the problem of defining appropriate lattice operators over such images.
0167-8655/$ - see front matter Ó 2013 Published by Elsevier B.V. http://dx.doi.org/10.1016/j.patrec.2013.06.006
The paper by Chyzhyk et al. is another instance of Lattice Computing approaches. The innovative computational construction is an ensemble of Dendritic Computing classifiers, trained by bootstrapping and fused by majority voting. This approach is applied in an active learning paradigm to the segmentation of CTA medical images of Abdominal Aortic Aneurysm, with the aim of providing an easy to use and quick interactive tool for the segmentation of these large data volumes. The paper of Papakostas et al. reveals a close relation between Lattice Computing and Fuzzy Systems modeling. The paper proposes an exhaustive and formalized review of intuitionistic fuzzy sets, with a detailed explanation of distance measures and an experimental illustration on benchmarking data of the relative advantages of each proposed distance. The paper by Burduk proposes an interval valued weighting of classifier results for their fusion regarding ensembles of classifiers. The advantage of this approach is that a measure of uncertainty is provided for each individual classifier and propagated to the final result. Approaches based on accuracy and error, dual views of the problem, are developed and tested on well known benchmark data. The paper by Bennasar et al. deals with an important aspect of knowledge representation for classification and reasoning, that of feature selection. The proposed approach looks for the greedy maximization of the interaction between features selected and candidates. The approach avoids combinatorial complexity explosion effectively and provides state of the art results. The paper by Ouzounisa et al. reports quality assessment results on an ongoing big data project for the determination of a global map of human settlements from multimodal remote sensing data. This is an excellent example of a real life application requiring the processing of large quantities of data with all kinds of data quality issues. The computational approach includes innovative parameter-less mathematical morphology and texture high resolution image analysis of multiple image modalities. The final classification layer is provided by statistical learning, offering an instance of innovative and real-life working combination of pattern recognition and image processing techniques. The paper by Priego et al. offers another example of remote sensing high dimensional image data processing. The main emphasis being in the proposal of a technique, which is inherently suitable for parallel implementation and extraordinary computational speed-ups, based on the classical cellular automata. The application of tailored evolution algorithms allows to domesticate the complexity of training such kind of systems for real life high dimensional image segmentation. The paper by Veganzones et al. deals with the construction of relevance feedback mechanisms to tailor the content based search
1568
Editorial / Pattern Recognition Letters 34 (2013) 1567–1568
in image databases to the user needs and requirements. The approach is based on the computation of dissimilarity spaces obtained from a two class classifier trained on the features given as the distances of the image to a set of prototypical images, that are iteratively selected from the database to narrow the search. Most informative prototypes are given by the uncertainty of the classification. The paper by Roy et al. introduces a novel knowledge based biclustering method for gene expression data, a hot topic in pattern recognition applications in the bioinformatics area. The single-pass property of the approach is rather enticing for the high computational demands imposed by this kind of application. Current growth of gene expression studies as a routine analysis for many research endeavors ask for such quick and accurate procedures. The paper by Sheen et al. goes into the real of biocultural inspiration for the development of innovative computational process, including pattern recognition demonstrated on the malware detection. The computational process is the musical harmony inspired optimization of parallel ensembles of classifiers. The approach effectively balances computational cost and accuracy, achieving state of the art results. The paper by Tian et al. proposes an innovative cascade classifier, combining Haar and shapelet features to test diverse conditions in pedestrian image sequences, specifically cascades of Haar feature based classifiers remove effectively background features, while the shapelet based classifiers are aimed to the detection of pedestrian shape features. The paper by Favorskaya et al. provides a rich combination of image processing and machine learning process for the reconstruction of background data in image sequences. The temporal and spatial processing involves the texture segmentation of the image sequence, the estimation of smoothness, structure and isotropy by bioinspired classifiers and the texton based image reconstruction. The paper by Sarlin is an exciting new approach to the analysis of the current economical and financial turmoil from an already established brand of pattern analysis tools, the famous Self-Organizing Map, upgraded with time series analysis similarity abilities. The approach is able to identify temporal structures signaling major turning points of our current and ongoing crisis, searching for solutions. The paper of Tedin et al. proposes an knowledge based innovation of the classical active shape segmentation by modeling the appearance of image contours by an innovative classification method, with exciting applications in human computer interfaces based on gestures. The paper by Maraval et al. proposes the fusion of learning automata and probabilistic knowledge based method for classification of images, including formal convergence proofs. The salient aspect of this contribution is its ability to learn continuously by
adapting the probabilistic representation while the stream of data is being received. The data granulation allows the flexibility of the object representation. The whole approach is applied to holistic image recognition for landmark based navigation. The paper by Ortiz et al. proposes another fusion of well known methods for the computed aided diagnosis of Alzheimer’s Disease, which is a high priority social and scientific challenge. The careful processing of the high dimensional data given in the 3D MRI volumes sets a methodological standard for such studies, which are contributing to the collective effort of finding non-invasive image biomarkers of the disease, allowing early diagnostic and improved life quality. The paper by Vilamala et al. addresses another very dramatic problem in medical information processing: the identification of brain tumor type by Magnetic Resonance Spectroscopy data analysis. Such data is analyzed by finding an adequate decomposition into the constituent sources. Addition of a priori knowledge to the conventional non-negative matrix factorization boots the performance of the process and subsequent tumor identification. The paper by Filipczuk et al. treats another kind of cancer, i.e. breast cancer, from the point of view of image classification of cytological samples. After an adaptive feature extraction process extracting the cell nucleus, a suite of random support classifiers is applied and the result is input to a trained classifier fuser. The approach provides an efficient and accurate computer based assistant to the human cytologist actually doing the diagnosis. The paper by Sanchez et al. proposes a comprehensive framework for computer assisted clinical decision systems. The system encompasses several information sources ranging from the medical literature to the clinical evaluations, analytical results and image biomarkers. The system aims to be minimally disruptive of the clinical decision making practice, therefore its output is given as a selection of reasoned recommendations with a support value. The system aims to be subjected to continuous learning adapted to the evolving clinical paradigm. We thank specially Dr. Gabriella Sanniti di Baja for her support and close follow up of the special issue editorial management. Manuel Graña Basque Country University UPV/EHU, Department of Computer Science and Artificial Intelligence, Spain Michal Wozniak Wroclaw University of Technology, Department of Systems and Computer Networks, Poland Nima Hatami University of California San Diego, Imaging Data Evaluation and Analysis (IDEA) Center, USA Available online 14 June 2013