Pattern recognition with sparse representation of covariance matrices andcovariance descriptors

У раду је предложен нови модел за ретку апроксимацију Гаусових компоненти у моделима за статистичко препознавање облика заснованим на Гаусовим смешама, а са циљем редукције сложености препознавања. Апроксимације инверзних коваријансних матрица конструишу се као ретке линеарне комбинације симетричних матрица из наученог редундантног скупа, коришћењем информационог критеријума који почива на принципу минимума дискриминативне информације. Ретка репрезентација подразумева релативно мали број активних компоненти приликом реконструкције сигнала, а тај циљ постиже тако што истовремено тежи: очувању информационог садржаја и једноставности представе или репрезентације.U radu je predložen novi model za retku aproksimaciju Gausovih komponenti u modelima za statističko prepoznavanje oblika zasnovanim na Gausovim smešama, a sa ciljem redukcije složenosti prepoznavanja. Aproksimacije inverznih kovarijansnih matrica konstruišu se kao retke linearne kombinacije simetričnih matrica iz naučenog redundantnog skupa, korišćenjem informacionog kriterijuma koji počiva na principu minimuma diskriminativne informacije. Retka reprezentacija podrazumeva relativno mali broj aktivnih komponenti prilikom rekonstrukcije signala, a taj cilj postiže tako što istovremeno teži: očuvanju informacionog sadržaja i jednostavnosti predstave ili reprezentacije.Paper presents a new model for sparse approximation of Gaussian components in statistical pattern recognition models that are based on Gaussian mixtures, with the aim of reducing computational complexity. Approximations of inverse covariance matrices are designed as sparse linear combinations of symmetric matrices that form redundant set, which is learned through information criterion based on the principle of minimum discrimination information. Sparse representation assumes relatively small number of active components in signal reconstruction, and it achieves that goal by simultaneously striving for: preservation of information content and simplicity of notion or representation

Pattern recognition with sparse representation of covariance matrices andcovariance descriptors

У раду је предложен нови модел за ретку апроксимацију Гаусових компоненти у моделима за статистичко препознавање облика заснованим на Гаусовим смешама, а са циљем редукције сложености препознавања. Апроксимације инверзних коваријансних матрица конструишу се као ретке линеарне комбинације симетричних матрица из наученог редундантног скупа, коришћењем информационог критеријума који почива на принципу минимума дискриминативне информације. Ретка репрезентација подразумева релативно мали број активних компоненти приликом реконструкције сигнала, а тај циљ постиже тако што истовремено тежи: очувању информационог садржаја и једноставности представе или репрезентације.U radu je predložen novi model za retku aproksimaciju Gausovih komponenti u modelima za statističko prepoznavanje oblika zasnovanim na Gausovim smešama, a sa ciljem redukcije složenosti prepoznavanja. Aproksimacije inverznih kovarijansnih matrica konstruišu se kao retke linearne kombinacije simetričnih matrica iz naučenog redundantnog skupa, korišćenjem informacionog kriterijuma koji počiva na principu minimuma diskriminativne informacije. Retka reprezentacija podrazumeva relativno mali broj aktivnih komponenti prilikom rekonstrukcije signala, a taj cilj postiže tako što istovremeno teži: očuvanju informacionog sadržaja i jednostavnosti predstave ili reprezentacije.Paper presents a new model for sparse approximation of Gaussian components in statistical pattern recognition models that are based on Gaussian mixtures, with the aim of reducing computational complexity. Approximations of inverse covariance matrices are designed as sparse linear combinations of symmetric matrices that form redundant set, which is learned through information criterion based on the principle of minimum discrimination information. Sparse representation assumes relatively small number of active components in signal reconstruction, and it achieves that goal by simultaneously striving for: preservation of information content and simplicity of notion or representation

Bootstrapped SSL CycleGAN for Asymmetric Domain Transfer

Most CycleGAN domain transfer architectures require a large amount of data belonging to domains on which the domain transfer task is to be applied. Nevertheless, in many real-world applications one of the domains is reduced, i.e., scarce. This means that it has much less training data available in comparison to the other domain, which is fully observable. In order to tackle the problem of using CycleGAN framework in such unfavorable application scenarios, we propose and invoke a novel Bootstrapped SSL CycleGAN architecture (BTS-SSL), where the mentioned problem is overcome using two strategies. Firstly, by using a relatively small percentage of available labelled training data from the reduced or scarce domain and a Semi-Supervised Learning (SSL) approach, we prevent overfitting of the discriminator belonging to the reduced domain, which would otherwise occur during initial training iterations due to the small amount of available training data in the scarce domain. Secondly, after initial learning guided by the described SSL strategy, additional bootstrapping (BTS) of the reduced data domain is performed by inserting artifically generated training examples into the training poll of the data discriminator belonging to the scarce domain. Bootstrapped samples are generated by the already trained neural network that performs transferring from the fully observable to the scarce domain. The described procedure is periodically repeated during the training process several times and results in significantly improved performance of the final model in comparison to the original unsupervised CycleGAN approach. The same also holds in comparison to the solutions that are exclusively based either on the described SSL, or on the bootstrapping strategy, i.e., when these are applied separately. Moreover, in the considered scarce scenarios it also shows competitive results in comparison to the fully supervised solution based on the pix2pix method. In that sense, it is directly applicable to many domain transfer tasks that are relying on the CycleGAN architecture

Bootstrapped SSL CycleGAN for Asymmetric Domain Transfer

Most CycleGAN domain transfer architectures require a large amount of data belonging to domains on which the domain transfer task is to be applied. Nevertheless, in many real-world applications one of the domains is reduced, i.e., scarce. This means that it has much less training data available in comparison to the other domain, which is fully observable. In order to tackle the problem of using CycleGAN framework in such unfavorable application scenarios, we propose and invoke a novel Bootstrapped SSL CycleGAN architecture (BTS-SSL), where the mentioned problem is overcome using two strategies. Firstly, by using a relatively small percentage of available labelled training data from the reduced or scarce domain and a Semi-Supervised Learning (SSL) approach, we prevent overfitting of the discriminator belonging to the reduced domain, which would otherwise occur during initial training iterations due to the small amount of available training data in the scarce domain. Secondly, after initial learning guided by the described SSL strategy, additional bootstrapping (BTS) of the reduced data domain is performed by inserting artifically generated training examples into the training poll of the data discriminator belonging to the scarce domain. Bootstrapped samples are generated by the already trained neural network that performs transferring from the fully observable to the scarce domain. The described procedure is periodically repeated during the training process several times and results in significantly improved performance of the final model in comparison to the original unsupervised CycleGAN approach. The same also holds in comparison to the solutions that are exclusively based either on the described SSL, or on the bootstrapping strategy, i.e., when these are applied separately. Moreover, in the considered scarce scenarios it also shows competitive results in comparison to the fully supervised solution based on the pix2pix method. In that sense, it is directly applicable to many domain transfer tasks that are relying on the CycleGAN architecture

Feature Map Regularized CycleGAN for Domain Transfer

CycleGAN domain transfer architectures use cycle consistency loss mechanisms to enforce the bijectivity of highly underconstrained domain transfer mapping. In this paper, in order to further constrain the mapping problem and reinforce the cycle consistency between two domains, we also introduce a novel regularization method based on the alignment of feature maps probability distributions. This type of optimization constraint, expressed via an additional loss function, allows for further reducing the size of the regions that are mapped from the source domain into the same image in the target domain, which leads to mapping closer to the bijective and thus better performance. By selecting feature maps of the network layers with the same depth d in the encoder of the direct generative adversarial networks (GANs), and the decoder of the inverse GAN, it is possible to describe their d-dimensional probability distributions and, through novel regularization term, enforce similarity between representations of the same image in both domains during the mapping cycle. We introduce several ground distances between Gaussian distributions of the corresponding feature maps used in the regularization. In the experiments conducted on several real datasets, we achieved better performance in the unsupervised image transfer task in comparison to the baseline CycleGAN, and obtained results that were much closer to the fully supervised pix2pix method for all used datasets. The PSNR measure of the proposed method was, on average, 4.7% closer to the results of the pix2pix method in comparison to the baseline CycleGAN over all datasets. This also held for SSIM, where the described percentage was 8.3% on average over all datasets

Measure of Similarity between GMMs Based on Autoencoder-Generated Gaussian Component Representations

A novel similarity measure between Gaussian mixture models (GMMs), based on similarities between the low-dimensional representations of individual GMM components and obtained using deep autoencoder architectures, is proposed in this paper. Two different approaches built upon these architectures are explored and utilized to obtain low-dimensional representations of Gaussian components in GMMs. The first approach relies on a classical autoencoder, utilizing the Euclidean norm cost function. Vectorized upper-diagonal symmetric positive definite (SPD) matrices corresponding to Gaussian components in particular GMMs are used as inputs to the autoencoder. Low-dimensional Euclidean vectors obtained from the autoencoder’s middle layer are then used to calculate distances among the original GMMs. The second approach relies on a deep convolutional neural network (CNN) autoencoder, using SPD representatives to generate embeddings corresponding to multivariate GMM components given as inputs. As the autoencoder training cost function, the Frobenious norm between the input and output layers of such network is used and combined with regularizer terms in the form of various pieces of information, as well as the Riemannian manifold-based distances between SPD representatives corresponding to the computed autoencoder feature maps. This is performed assuming that the underlying probability density functions (PDFs) of feature-map observations are multivariate Gaussians. By employing the proposed method, a significantly better trade-off between the recognition accuracy and the computational complexity is achieved when compared with other measures calculating distances among the SPD representatives of the original Gaussian components. The proposed method is much more efficient in machine learning tasks employing GMMs and operating on large datasets that require a large overall number of Gaussian components

Image Processing Method for Automatic Discrimination of Hoverfly Species

An approach to automatic hoverfly species discrimination based on detection and extraction of vein junctions in wing venation patterns of insects is presented in the paper. The dataset used in our experiments consists of high resolution microscopic wing images of several hoverfly species collected over a relatively long period of time at different geographic locations. Junctions are detected using the combination of the well known HOG (histograms of oriented gradients) and the robust version of recently proposed CLBP (complete local binary pattern). These features are used to train an SVM classifier to detect junctions in wing images. Once the junctions are identified they are used to extract statistics characterizing the constellations of these points. Such simple features can be used to automatically discriminate four selected hoverfly species with polynomial kernel SVM and achieve high classification accuracy

Lossy Compression of Multispectral Satellite Images with Application to Crop Thematic Mapping: A HEVC Comparative Study

Remote sensing applications have gained in popularity in recent years, which has resulted in vast amounts of data being produced on a daily basis. Managing and delivering large sets of data becomes extremely difficult and resource demanding for the data vendors, but even more for individual users and third party stakeholders. Hence, research in the field of efficient remote sensing data handling and manipulation has become a very active research topic (from both storage and communication perspectives). Driven by the rapid growth in the volume of optical satellite measurements, in this work we explore the lossy compression technique for multispectral satellite images. We give a comprehensive analysis of the High Efficiency Video Coding (HEVC) still-image intra coding part applied to the multispectral image data. Thereafter, we analyze the impact of the distortions introduced by the HEVC’s intra compression in the general case, as well as in the specific context of crop classification application. Results show that HEVC’s intra coding achieves better trade-off between compression gain and image quality, as compared to standard JPEG 2000 solution. On the other hand, this also reflects in the better performance of the designed pixel-based classifier in the analyzed crop classification task. We show that HEVC can obtain up to 150:1 compression ratio, when observing compression in the context of specific application, without significantly losing on classification performance compared to classifier trained and applied on raw data. In comparison, in order to maintain the same performance, JPEG 2000 allows compression ratio up to 70:1