Toward a General-Purpose Heterogeneous Ensemble for Pattern Classification

We perform an extensive study of the performance of different classification approaches on twenty-five datasets (fourteen image datasets and eleven UCI data mining datasets). The aim is to find General-Purpose (GP) heterogeneous ensembles (requiring little to no parameter tuning) that perform competitively across multiple datasets. The state-of-the-art classifiers examined in this study include the support vector machine, Gaussian process classifiers, random subspace of adaboost, random subspace of rotation boosting, and deep learning classifiers. We demonstrate that a heterogeneous ensemble based on the simple fusion by sum rule of different classifiers performs consistently well across all twenty-five datasets. The most important result of our investigation is demonstrating that some very recent approaches, including the heterogeneous ensemble we propose in this paper, are capable of outperforming an SVM classifier (implemented with LibSVM), even when both kernel selection and SVM parameters are carefully tuned for each dataset

Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification

Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The d facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Binary Patterns encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Our final combination outperforms the state-of-the-art without employing fine-tuning or ensemble of RGB network architectures.Comment: To appear in ISPRS Journal of Photogrammetry and Remote Sensin

Autoencoding the Retrieval Relevance of Medical Images

Content-based image retrieval (CBIR) of medical images is a crucial task that can contribute to a more reliable diagnosis if applied to big data. Recent advances in feature extraction and classification have enormously improved CBIR results for digital images. However, considering the increasing accessibility of big data in medical imaging, we are still in need of reducing both memory requirements and computational expenses of image retrieval systems. This work proposes to exclude the features of image blocks that exhibit a low encoding error when learned by a $n/p/n$ autoencoder ($p\!<\!n$). We examine the histogram of autoendcoding errors of image blocks for each image class to facilitate the decision which image regions, or roughly what percentage of an image perhaps, shall be declared relevant for the retrieval task. This leads to reduction of feature dimensionality and speeds up the retrieval process. To validate the proposed scheme, we employ local binary patterns (LBP) and support vector machines (SVM) which are both well-established approaches in CBIR research community. As well, we use IRMA dataset with 14,410 x-ray images as test data. The results show that the dimensionality of annotated feature vectors can be reduced by up to 50% resulting in speedups greater than 27% at expense of less than 1% decrease in the accuracy of retrieval when validating the precision and recall of the top 20 hits.Comment: To appear in proceedings of The 5th International Conference on Image Processing Theory, Tools and Applications (IPTA'15), Nov 10-13, 2015, Orleans, Franc

Ensemble of Different Approaches for a Reliable Person Re-identification System

An ensemble of approaches for reliable person re-identification is proposed in this paper. The proposed ensemble is built combining widely used person re-identification systems using different color spaces and some variants of state-of-the-art approaches that are proposed in this paper. Different descriptors are tested, and both texture and color features are extracted from the images; then the different descriptors are compared using different distance measures (e.g., the Euclidean distance, angle, and the Jeffrey distance). To improve performance, a method based on skeleton detection, extracted from the depth map, is also applied when the depth map is available. The proposed ensemble is validated on three widely used datasets (CAVIAR4REID, IAS, and VIPeR), keeping the same parameter set of each approach constant across all tests to avoid overfitting and to demonstrate that the proposed system can be considered a general-purpose person re-identification system. Our experimental results show that the proposed system offers significant improvements over baseline approaches. The source code used for the approaches tested in this paper will be available at https://www.dei.unipd.it/node/2357 and http://robotics.dei.unipd.it/reid/

Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields

This work presents a first evaluation of using spatio-temporal receptive fields from a recently proposed time-causal spatio-temporal scale-space framework as primitives for video analysis. We propose a new family of video descriptors based on regional statistics of spatio-temporal receptive field responses and evaluate this approach on the problem of dynamic texture recognition. Our approach generalises a previously used method, based on joint histograms of receptive field responses, from the spatial to the spatio-temporal domain and from object recognition to dynamic texture recognition. The time-recursive formulation enables computationally efficient time-causal recognition. The experimental evaluation demonstrates competitive performance compared to state-of-the-art. Especially, it is shown that binary versions of our dynamic texture descriptors achieve improved performance compared to a large range of similar methods using different primitives either handcrafted or learned from data. Further, our qualitative and quantitative investigation into parameter choices and the use of different sets of receptive fields highlights the robustness and flexibility of our approach. Together, these results support the descriptive power of this family of time-causal spatio-temporal receptive fields, validate our approach for dynamic texture recognition and point towards the possibility of designing a range of video analysis methods based on these new time-causal spatio-temporal primitives.Comment: 29 pages, 16 figure

Offline Handwritten Signature Verification - Literature Review

The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5-10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory, Tools and Applications (IPTA 2017