Proceedings of the 2019 2nd International Conference on Watermarking and Image Processing, 2019
In this paper, a simple and robust watermarking algorithm is presented by using the first, second... more In this paper, a simple and robust watermarking algorithm is presented by using the first, second, third and the fourth Least Significant Bits (LSBs). We embed two bits in two places out of four LSBs according to the local variance value. Compared to the simple LSB algorithm where we use bits 7 and 8 to embed information, the proposed algorithm is more robust to white noise and JPEG compression. Experimental results show that the quality of the watermarked image is high in terms of Peak Signal-to-Noise (PSNR) and Structural Similarity Index (SSIM).
⎯ A data warehouse is a special database used for storing business oriented information for futur... more ⎯ A data warehouse is a special database used for storing business oriented information for future analysis and decision-making. In business scenarios, where some of the data or the business attributes are fuzzy, it may be useful to construct a warehouse that can support the analysis of fuzzy data. Here, we outline how Kimball‘s methodology for the design of a data warehouse can be extended to the construction of a fuzzy data warehouse. A case study demonstrates the viability of the methodology.
Most Statistical Process Control (SPC) methods are not suitable for monitoring non-linear and sta... more Most Statistical Process Control (SPC) methods are not suitable for monitoring non-linear and state-dependent processes. This paper introduces the Context-based SPC (CSPC) methodology for state-dependent data generated by a finite-memory source. The key idea of the CSPC is to monitor the statistical attributes of a process by comparing two context trees at any monitoring period of time. The first is a reference tree that represents the 'in control' reference behaviour of the process; the second is a monitored tree, generated periodically from a sample of sequenced observations, that represents the behaviour of the process at that period. The Kullback-Leibler (KL) statistic is used to measure the relative 'distance' between these two trees, and an analytic distribution of this statistic is derived. Monitoring the KL statistic indicates whether there has been any significant change in the process that requires intervention. An example of buffer-level monitoring in a pr...
The Relevance Vector Machine (RVM) is a generalized linear model that can use kernel functions as... more The Relevance Vector Machine (RVM) is a generalized linear model that can use kernel functions as basis functions. The typical RVM solution is very sparse. We present a strategy for feature ranking and selection via evaluating the influence of the features on the relevance vectors. This requires a single training of the RVM, thus, it is very efficient. Experiments on a benchmark regression problem provide evidence that it selects high-quality feature sets at a fraction of the costs of classical methods. Key-Words: Feature Selection, Relevance Vector Machine, Machine Learning
Image understanding relies heavily on accurate multilabel classification. In recent years, deep l... more Image understanding relies heavily on accurate multilabel classification. In recent years, deep learning (DL) algorithms have become very successful tools for multi-label classification of image objects, and various implementations of DL algorithms have been released for public use in the form of application programming interfaces (APIs). In this study, we evaluate and compare 10 of the most prominent publicly available APIs in a best-of-breed challenge. The evaluation is performed on the Visual Genome labeling benchmark dataset using 12 well-recognized similarity metrics. In addition, for the first time in this kind of comparison, we use a semantic similarity metric to evaluate the semantic similarity performance of these APIs. In this evaluation, Microsoft’s Computer Vision, TensorFlow, Imagga, and IBM’s Visual Recognition performed better than the other APIs. Furthermore, the new semantic similarity metric provided deeper insights for comparison. Keywords— multi-label classificat...
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2017
Automatic generation of natural language descriptions for images has recently become an important... more Automatic generation of natural language descriptions for images has recently become an important research topic. In this paper, we propose a frame-based algorithm for generating a composite natural language description for a given image. The goal of this algorithm is to describe not only the objects appearing in the image but also the main activities happening in the image and the objects participating in those activities. The algorithm builds upon a pre-trained CRF (Conditional Random Field)-based structured prediction model, which generates a set of alternative frames for a given image. We use imSitu, a situation recognition dataset with 126,102 images, 504 activities, 11,538 objects, and 1,788 roles, as a test bed of our algorithm. We ask human evaluators to evaluate the quality of the descriptions for 20 images from the imSitu dataset. The results demonstrate that our composite description contains on average 16% more visual elements than the baseline method and gains a signifi...
With the rapidly increasing number of online video resources, the ability of automatically unders... more With the rapidly increasing number of online video resources, the ability of automatically understanding those videos becomes more and more important, since it is almost impossible for people to watch all of the videos and provide textual descriptions. The duration of online videos varies in a extremely wide range, from several seconds to more than 5 h. In this paper, we focus on long videos, especially on full-length movies, and propose the first pipeline for automatically generating textual summaries of such movies. The proposed system takes an entire movie as input (including subtitles), splits it into scenes, generates a one-sentence description for each scene and summarizes those descriptions and subtitles into a final summary. In our initial experiment on a popular cinema movie (Forrest Gump), we utilize several existing algorithms and software tools for implementing the different components of our system. Most importantly, we use the S2VT (Sequence to Sequence—Video to Text) ...
Image understanding relies heavily on accurate multi-label classification. In recent years deep l... more Image understanding relies heavily on accurate multi-label classification. In recent years deep learning (DL) algorithms have become very successful tools for multi-label classification of image objects. With these set of tools, various implementations of DL algorithms have been released for the public use in the form of application programming interfaces (API). In this study, we evaluate and compare 10 of the most prominent publicly available APIs in a best-of-breed challenge. The evaluation is performed on the Visual Genome labeling benchmark dataset using 12 well-recognized similarity metrics. In addition, for the first time in this kind of comparison, we use a semantic similarity metric to evaluate the semantic similarity performance of these APIs. In this evaluation, Microsoft's Computer Vision, TensorFlow, Imagga, and IBM's Visual Recognition showed better performance than the other APIs. Furthermore, the new semantic similarity metric allowed deeper insights for compa...
Data mining and knowledge discovery handbook, 2005
Support Vector Machines (SVMs) are a set of related methods for supervised learning, applicable t... more Support Vector Machines (SVMs) are a set of related methods for supervised learning, applicable to both classification and regression problems. A SVM classifiers creates a maximum-margin hyperplane that lies in a transformed input space and splits the example ...
Finding an appropriatetrade-off between performanceand computational complexity is an important i... more Finding an appropriatetrade-off between performanceand computational complexity is an important issue in the design of adaptive algorithms. This paper introduces an algorithm for adaptive identification of Non-linear Auto-Regressive with eXogenous inputs (NARX) ...
Proceedings of the 2019 2nd International Conference on Watermarking and Image Processing, 2019
In this paper, a simple and robust watermarking algorithm is presented by using the first, second... more In this paper, a simple and robust watermarking algorithm is presented by using the first, second, third and the fourth Least Significant Bits (LSBs). We embed two bits in two places out of four LSBs according to the local variance value. Compared to the simple LSB algorithm where we use bits 7 and 8 to embed information, the proposed algorithm is more robust to white noise and JPEG compression. Experimental results show that the quality of the watermarked image is high in terms of Peak Signal-to-Noise (PSNR) and Structural Similarity Index (SSIM).
⎯ A data warehouse is a special database used for storing business oriented information for futur... more ⎯ A data warehouse is a special database used for storing business oriented information for future analysis and decision-making. In business scenarios, where some of the data or the business attributes are fuzzy, it may be useful to construct a warehouse that can support the analysis of fuzzy data. Here, we outline how Kimball‘s methodology for the design of a data warehouse can be extended to the construction of a fuzzy data warehouse. A case study demonstrates the viability of the methodology.
Most Statistical Process Control (SPC) methods are not suitable for monitoring non-linear and sta... more Most Statistical Process Control (SPC) methods are not suitable for monitoring non-linear and state-dependent processes. This paper introduces the Context-based SPC (CSPC) methodology for state-dependent data generated by a finite-memory source. The key idea of the CSPC is to monitor the statistical attributes of a process by comparing two context trees at any monitoring period of time. The first is a reference tree that represents the 'in control' reference behaviour of the process; the second is a monitored tree, generated periodically from a sample of sequenced observations, that represents the behaviour of the process at that period. The Kullback-Leibler (KL) statistic is used to measure the relative 'distance' between these two trees, and an analytic distribution of this statistic is derived. Monitoring the KL statistic indicates whether there has been any significant change in the process that requires intervention. An example of buffer-level monitoring in a pr...
The Relevance Vector Machine (RVM) is a generalized linear model that can use kernel functions as... more The Relevance Vector Machine (RVM) is a generalized linear model that can use kernel functions as basis functions. The typical RVM solution is very sparse. We present a strategy for feature ranking and selection via evaluating the influence of the features on the relevance vectors. This requires a single training of the RVM, thus, it is very efficient. Experiments on a benchmark regression problem provide evidence that it selects high-quality feature sets at a fraction of the costs of classical methods. Key-Words: Feature Selection, Relevance Vector Machine, Machine Learning
Image understanding relies heavily on accurate multilabel classification. In recent years, deep l... more Image understanding relies heavily on accurate multilabel classification. In recent years, deep learning (DL) algorithms have become very successful tools for multi-label classification of image objects, and various implementations of DL algorithms have been released for public use in the form of application programming interfaces (APIs). In this study, we evaluate and compare 10 of the most prominent publicly available APIs in a best-of-breed challenge. The evaluation is performed on the Visual Genome labeling benchmark dataset using 12 well-recognized similarity metrics. In addition, for the first time in this kind of comparison, we use a semantic similarity metric to evaluate the semantic similarity performance of these APIs. In this evaluation, Microsoft’s Computer Vision, TensorFlow, Imagga, and IBM’s Visual Recognition performed better than the other APIs. Furthermore, the new semantic similarity metric provided deeper insights for comparison. Keywords— multi-label classificat...
2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2017
Automatic generation of natural language descriptions for images has recently become an important... more Automatic generation of natural language descriptions for images has recently become an important research topic. In this paper, we propose a frame-based algorithm for generating a composite natural language description for a given image. The goal of this algorithm is to describe not only the objects appearing in the image but also the main activities happening in the image and the objects participating in those activities. The algorithm builds upon a pre-trained CRF (Conditional Random Field)-based structured prediction model, which generates a set of alternative frames for a given image. We use imSitu, a situation recognition dataset with 126,102 images, 504 activities, 11,538 objects, and 1,788 roles, as a test bed of our algorithm. We ask human evaluators to evaluate the quality of the descriptions for 20 images from the imSitu dataset. The results demonstrate that our composite description contains on average 16% more visual elements than the baseline method and gains a signifi...
With the rapidly increasing number of online video resources, the ability of automatically unders... more With the rapidly increasing number of online video resources, the ability of automatically understanding those videos becomes more and more important, since it is almost impossible for people to watch all of the videos and provide textual descriptions. The duration of online videos varies in a extremely wide range, from several seconds to more than 5 h. In this paper, we focus on long videos, especially on full-length movies, and propose the first pipeline for automatically generating textual summaries of such movies. The proposed system takes an entire movie as input (including subtitles), splits it into scenes, generates a one-sentence description for each scene and summarizes those descriptions and subtitles into a final summary. In our initial experiment on a popular cinema movie (Forrest Gump), we utilize several existing algorithms and software tools for implementing the different components of our system. Most importantly, we use the S2VT (Sequence to Sequence—Video to Text) ...
Image understanding relies heavily on accurate multi-label classification. In recent years deep l... more Image understanding relies heavily on accurate multi-label classification. In recent years deep learning (DL) algorithms have become very successful tools for multi-label classification of image objects. With these set of tools, various implementations of DL algorithms have been released for the public use in the form of application programming interfaces (API). In this study, we evaluate and compare 10 of the most prominent publicly available APIs in a best-of-breed challenge. The evaluation is performed on the Visual Genome labeling benchmark dataset using 12 well-recognized similarity metrics. In addition, for the first time in this kind of comparison, we use a semantic similarity metric to evaluate the semantic similarity performance of these APIs. In this evaluation, Microsoft's Computer Vision, TensorFlow, Imagga, and IBM's Visual Recognition showed better performance than the other APIs. Furthermore, the new semantic similarity metric allowed deeper insights for compa...
Data mining and knowledge discovery handbook, 2005
Support Vector Machines (SVMs) are a set of related methods for supervised learning, applicable t... more Support Vector Machines (SVMs) are a set of related methods for supervised learning, applicable to both classification and regression problems. A SVM classifiers creates a maximum-margin hyperplane that lies in a transformed input space and splits the example ...
Finding an appropriatetrade-off between performanceand computational complexity is an important i... more Finding an appropriatetrade-off between performanceand computational complexity is an important issue in the design of adaptive algorithms. This paper introduces an algorithm for adaptive identification of Non-linear Auto-Regressive with eXogenous inputs (NARX) ...
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Papers by Armin Shmilovici