International Journal of Pattern Recognition and Artificial Intelligence

Most existing feature selection methods focus on ranking individual features based on a utility criterion, and select the optimal feature set in a greedy manner. However, the feature combinations found in this way do not give optimal classification performance, since they neglect the correlations among features. In an attempt to overcome this problem, we develop a novel feature selection technique using the spectral data transformation and by using ℓ₁-norm regularized models for subset selection. Specifically, we propose a new two-step spectral regression technique for unsupervised feature selection. In the first step, we use kernel entropy component analysis (kECA) to transform the data into a lower-dimensional space so as to improve class separation. Second, we use ℓ₁-norm regularization to select the features that best align with the data embedding resulting from kECA. The advantage of kECA is that dimensionality reducing data transformation maximally preserves entropy estimates for the input data whilst also best preserving the cluster structure of the data. Using ℓ₁-norm regularization, we cast feature discriminant analysis into a regression framework which accommodates the correlations among features. As a result, we can evaluate joint feature combinations, rather than being confined to consider them individually. Experimental results demonstrate the effectiveness of our feature selection method on a number of standard face datasets.

Gene-expression microarray datasets often consist of a limited number of samples with a large number of gene-expression measurements, usually on the order of thousands. Therefore, dimensionality reduction is critical prior to any classification task. In this work, the iterative feature perturbation method (IFP), an embedded gene selector, is introduced and applied to four microarray cancer datasets: colon cancer, leukemia, Moffitt colon cancer, and lung cancer. We compare results obtained by IFP to those of support vector machine-recursive feature elimination (SVM-RFE) and the t-test as a feature filter using a linear support vector machine as the base classifier. Analysis of the intersection of gene sets selected by the three methods across the four datasets was done. Additional experiments included an initial pre-selection of the top 200 genes based on their p values. IFP and SVM-RFE were then applied on the reduced feature sets. These results showed up to 3.32% average performance improvement for IFP across the four datasets. A statistical analysis (using the Friedman/Holm test) for both scenarios showed the highest accuracies came from the t-test as a filter on experiments without gene pre-selection. IFP and SVM-RFE had greater classification accuracy after gene pre-selection. Analysis showed the t-test is a good gene selector for microarray data. IFP and SVM-RFE showed performance improvement on a reduced by t-test dataset. The IFP approach resulted in comparable or superior average class accuracy when compared to SVM-RFE on three of the four datasets. The same or similar accuracies can be obtained with different sets of genes.

We model the edit distance as a function in a labeling space. A labeling space is an Euclidean space where coordinates are the edit costs. Through this model, we define a class of cost. A class of cost is a region in the labeling space that all the edit costs have the same optimal labeling. Moreover, we characterize the distance value through the labeling space. This new point of view of the edit distance gives us the opportunity of defining some interesting properties that are useful for a better understanding of the edit distance. Finally, we show the usefulness of these properties through some applications.

Structures and features are opposite approaches in building representations for object recognition. Bridging the two is an essential problem in pattern recognition as the two opposite types of information are fundamentally different. As dissimilarities can be computed for both the dissimilarity representation can be used to combine the two. Attributed graphs contain structural as well as feature-based information. Neglecting the attributes yields a pure structural description. Isolating the features and neglecting the structure represents objects by a bag of features. In this paper we will show that weighted combinations of dissimilarities may perform better than these two extremes, indicating that these two types of information are essentially different and strengthen each other. In addition we present two more advanced integrations than weighted combining and show that these may improve the classification performances even further.

Most traditional postcode recognition systems implicitly assumed that the distribution of the 10 numerals (0–9) is balanced. However it is far from a reasonable setting because the distribution of 0–9 in postcodes of a country or a city is generally imbalanced. Some numerals appear in more postcodes, while some others do not. In this paper, we study cost-sensitive neural network classifiers to address the class imbalance problem in postcode recognition. Four methods, namely: cost-sampling, cost-convergence, rate-adapting and threshold-moving are considered in training neural networks. Cost-sampling adjusts the distribution of the training data such that the costs of classes are conveyed explicitly by the appearances of their instances. Cost-convergence and rate-adapting are carried out in training phase by modifying the architecture of training algorithms of the neural network. Threshold-moving tries to increase the probability estimations of expensive classes to avoid the samples with higher costs to be misclassified. 10,702 postcode images are experimented using five cost matrices based on the distribution of numerals in postcodes. The results suggest that cost-sensitive learning is indeed effective on class imbalanced postcode analysis and recognition. It also reveals that cost-sampling on a proper cost matrix outperforms others in this application.

Word spotting is the process of retrieving all instances of a queried keyword from a digital library of document images. In this paper we evaluate the performance of different word descriptors to assess the advantages and disadvantages of statistical and structural models in a framework of query-by-example word spotting in historical documents. We compare four word representation models, namely sequence alignment using DTW as a baseline reference, a bag of visual words approach as statistical model, a pseudo-structural model based on a Loci features representation, and a structural approach where words are represented by graphs. The four approaches have been tested with two collections of historical data: the George Washington database and the marriage records from the Barcelona Cathedral. We experimentally demonstrate that statistical representations generally give a better performance, however it cannot be neglected that large descriptors are difficult to be implemented in a retrieval scenario where word spotting requires the indexation of data with million word images.

Currently, automatic handwriting recognition systems are ineffectual in unconstrained handwriting documents. Therefore, to obtain perfect transcriptions, heavy human intervention is required to validate and correct the results of such systems. Given that this post-editing process is inefficient and uncomfortable, a multimodal interactive approach has been proposed in previous works, which aims at obtaining correct transcriptions with the minimum human effort. In this approach, the user interacts with the system by means of an e-pen and/or more traditional methods such as keyboard or mouse. This user's feedback allows to improve system accuracy and multimodality increases system ergonomics and user acceptability. Until now, multimodal interaction has been considered only at whole-word level. In this work, multimodal interaction at character-level is studied, that may lead to more effective interactivity, since it is faster and easier to write only one character rather than a whole word. Here we study this kind of fine-grained multimodal interaction and present developments that allow taking advantage of interaction-derived context to significantly improve feedback decoding accuracy. Empirical tests on three cursive handwritten tasks suggest that, despite losing the deterministic accuracy of traditional peripherals, this approach can save significant amounts of user effort with respect to fully manual transcription as well as to noninteractive post-editing correction.

In this paper we describe a system which assists knowledge workers in making notes of their thoughts and transferring them to the computer. Our system processes handwritten notes written down with a digital pen. These notes are processed in order to recognize and understand their meaning. To realize such systems, two novel processing stages are proposed for the first time in literature. The first stage is the inclusion of knowledge bases into the Handwriting Recognition (HWR) process, where we make use of a person's mental model. The second stage is the transition from pure HWR to understanding of the handwritten notes, i.e. the system extracts knowledge in form of ontologies. For both novel approaches we performed a set of experiments on various data. With the proposed techniques, the recognition rate of the HWR system as well as the performance of the information extraction system are significantly increased.

The region-based segmentation paradigm is a well known technique for image segmentation. In the first part of this work the robustness of region-based algorithms is studied. It is shown that within a small parameter range, which leads to good segmentation results in the majority of cases, bad segmentation results may occur. In fact, such local instability is a problem of region-based methods and reasons for its occurrence are discussed. In the second part of the work, an ensemble solution for this problem based on the median concept is proposed. Two variants, set median and generalized median, are presented and experimentally compared. Extensive experimental results demonstrate the potential of the proposed median approach for solving the instability problem.

Driven by key law enforcement and commercial applications, research on face recognition from video sources has intensified in recent years. The ensuing results have demonstrated that videos possess unique properties that allow both humans and automated systems to perform recognition accurately in difficult viewing conditions. However, significant research challenges remain as most video-based applications do not allow for controlled recordings. In this survey, we categorize the research in this area and present a broad and deep review of recently proposed methods for overcoming the difficulties encountered in unconstrained settings. We also draw connections between the ways in which humans and current algorithms recognize faces. An overview of the most popular and difficult publicly available face video databases is provided to complement these discussions. Finally, we cover key research challenges and opportunities that lie ahead for the field as a whole.