Decision fusion of horizontal and vertical trajectories for recognition of online Farsi subwords

Online handwriting is formed by a combination of horizontal and vertical trajectories. If these trajectories are treated separately, new recognition methods are emerged. In contrast, one classifier is often used to recognize handwriting. In this work, some features for x(t) and y(t) signals were proposed and used to make two separate classifiers. After initial recognition by these classifiers, their results were fused for final recognition. Using HMM classifiers and simple product rule for decision fusion, the recognition results of 42 classes of Farsi subwords showed promising achievements.     

Handling noise in textual image resolution enhancement using online and offline learned dictionaries

International Journal on Document Analysis and Recognition (IJDAR) - The resolution enhancement of textual images poses a significant challenge mainly in the presence of noise. The inherent...     

Lexicon reduction using key characters in cursive handwritten words

The concept of key characters in a cursively handwritten word image is introduced and a method for extracting the key characters is presented. Key characters capture the unambiguous parts of the cursive words that can be reliably segmented and recognized. We propose a method for lexicon reduction using key characters in conjunction with a word-length estimation.     

Integrating textual and pictorial information via pop-up windows: An experimental study

Following previous research in cognitive psychology, this paper deals with the effect of the spatial display of textpicture information on the user's cognitive processes. Two experiments were carried out to compare three displays on a computer screen: 'split' display (text and picture information displayed in separate areas on the screen), 'integrated' display (text information close to the part of the picture to which it refers), and 'pop-up' display (text information integrated in pop-up fields which appeared only via the user's action). In both experiments, the results showed that the integrated display and to a greater extent the pop-up display led to higher performances for an equal or lower learning time. Thus, these experiments reinforce the hypothesis that material where text and picture are integrated improves learning, especially if text information appears in pop-up fields. Results are discussed from a theoretical and a practical point of view.     

Integrating textual and pictorial information via pop-up windows: an experimental study

Following previous research in cognitive psychology, this paper deals with the effect of the spatial display of textpicture information on the user's cognitive processes. Two experiments were carried out to compare three displays on a computer screen: 'split' display (text and picture information displayed in separate areas on the screen), 'integrated' display (text information close to the part of the picture to which it refers), and 'pop-up' display (text information integrated in pop-up fields which appeared only via the user's action). In both experiments, the results showed that the integrated display and to a greater extent the pop-up display led to higher performances for an equal or lower learning time. Thus, these experiments reinforce the hypothesis that material where text and picture are integrated improves learning, especially if text information appears in pop-up fields. Results are discussed from a theoretical and a practical point of view.     

Segmentation of melanocytic skin lesions using feature learning and dictionaries

Pre-screening systems for the diagnosis of melanocytic skin lesions depend of the proper segmentation of the image region affected by the lesion. This paper proposes a feature learning scheme that finds relevant features for skin lesion image segmentation. This work introduces a new unsupervised dictionary learning method, namely Unsupervised Information-Theoretic Dictionary Learning (UITDL), and discusses how it can be applied in the segmentation of skin lesions in macroscopic images. The UITDL approach is adaptive and tends to be robust to outliers in the training data, and consists of two main stages. In the first stage, a textural variation image is used to construct an initial feature dictionary and an initial sparse representation via Non-Negative Matrix Factorization (NMF). In the second stage, the feature dictionary is optimized by selecting adaptively the number of dictionary atoms. The greedy approach used for dictionary optimization is quite efficient and flexible enough to be applied to other dictionary learning problems. Furthermore, the proposed method can be easily extended for other image segmentation problems. The experimental results suggest that the proposed approach potentially can provide more accurate skin lesion segmentation results than comparable state-of-the-art methods. The proposed segmentation method could help to improve the performance of pre-screening systems for melanocytic skin lesions, which can affect positively the quality of the early diagnosis provided to skin lesion patients.     

Recovering test-to-code traceability using slicing and textual analysis

Test suites are a valuable source of up-to-date documentation as developers continuously modify them to reflect changes in the production code and preserve an effective regression suite. While maintaining traceability links between unit test and the classes under test can be useful to selectively retest code after a change, the value of having traceability links goes far beyond this potential savings. One key use is to help developers better comprehend the dependencies between tests and classes and help maintain consistency during refactoring. Despite its importance, test-to-code traceability is not common in software development and, when needed, traceability information has to be recovered during software development and evolution. We propose an advanced approach, named SCOTCH+ (Source code and COncept based Test to Code traceability Hunter), to support the developer during the identification of links between unit tests and tested classes. Given a test class, represented by a JUnit class, the approach first exploits dynamic slicing to identify a set of candidate tested classes. Then, external and internal textual information associated with the classes retrieved by slicing is analyzed to refine this set of classes and identify the final set of candidate tested classes. The external information is derived from the analysis of the class name, while internal information is derived from identifiers and comments. The approach is evaluated on five software systems. The results indicate that the accuracy of the proposed approach far exceeds the leading techniques found in the literature.     

Multimedia ontology matching by using visual and textual modalities

Ontologies have been intensively applied for improving multimedia search and retrieval by providing explicit meaning to visual content. Several multimedia ontologies have been recently proposed as knowledge models suitable for narrowing the well known semantic gap and for enabling the semantic interpretation of images. Since these ontologies have been created in different application contexts, establishing links between them, a task known as ontology matching, promises to fully unlock their potential in support of multimedia search and retrieval. This paper proposes and compares empirically two extensional ontology matching techniques applied to an important semantic image retrieval issue: automatically associating common-sense knowledge to multimedia concepts. First, we extend a previously introduced textual concept matching approach to use both textual and visual representation of images. In addition, a novel matching technique based on a multi-modal graph is proposed. We argue that the textual and visual modalities have to be seen as complementary rather than as exclusive sources of extensional information in order to improve the efficiency of the application of an ontology matching approach in the multimedia domain. An experimental evaluation is included in the paper.     

Iconic pictorial retrieval using multiple attributes and spatial relationships

This work is on the use of multiple attributes or features and spatial relationships, with the help of a user interface based on an iconic paradigm, to retrieve images represented by iconic pictures. An icon has texture, color, and text attributes. Texture is represented by three statistical textural properties, namely, coarseness, contrast, and directionality. For text, the vector space model is used. For color, a representation based on a modified color histogram method which is less storage-intensive is proposed. The final icon similarity is the combination of the attribute similarity values using a proven adaptive algorithm. 2-D strings and its variants are commonly used to represent spatial relationships and perform spatial reasoning. We extended the method to include similarity ranking by using different similarity functions for different spatial relationships and an efficient embedding algorithm. Furthermore, our method solves the problem of query expressiveness which all methods based on 2-D string representations suffer from.     

Integration of textual cues for fine-grained image captioning using deep CNN and LSTM

Neural Computing and Applications - The automatic narration of a natural scene is an important trait in artificial intelligence that unites computer vision and natural language processing. Caption...     

基于统计与词汇语义特征的中文文本蕴涵识别

对中文这种意合型语言而言,为了进行文本内容理解和文本语义推理,必须识别文本间的蕴涵关系.针对中文文本,在文本预处理的基础上,提取中文文本的相关统计特征和词汇语义特征;基于获取的统计与词汇语义特征,使用支持向量机设计并实现分类器对中文文本对间蕴涵关系进行分类.实验结果表明,基于统计与词汇语义特征进行中文文本蕴涵关系识别是可行的.     

Conceptual feature generation for textual information using a conceptual network constructed from Wikipedia

A proper semantic representation of textual information underlies many natural language processing tasks. In this paper, a novel semantic annotator is presented to generate conceptual features for text documents. A comprehensive conceptual network is automatically constructed with the aid of Wikipedia that has been represented as a Markov chain. Furthermore, semantic annotator gets a fragment of natural language text and initiates a random walk to generate conceptual features that represent topical semantic of the input text. The generated conceptual features are applicable to many natural language processing tasks where the input is textual information and the output is a decision based on its context. Consequently, the effectiveness of the generated features is evaluated in the task of document clustering and classification. Empirical results demonstrate that representing text using conceptual features and considering the relations between concepts can significantly improve not only the bag of words representation but also other state‐of‐the‐art approaches.     

基于双语信息和标签传播算法的中文情感词典构建方法

文本情感分析是目前自然语言处理领域的一个热点研究问题,具有广泛的实用价值和理论研究意义。情感词典构建则是文本情感分析的一项基础任务,即将词语按照情感倾向分为褒义、中性或者贬义。然而,中文情感词典构建存在两个主要问题 1)许多情感词存在多义、歧义的现象,即一个词语在不同语境中它的语义倾向也不尽相同,这给词语的情感计算带来困难;2)由国内外相关研究现状可知,中文情感字典建设的可用资源相对较少。考虑到英文情感分析研究中存在大量语料和词典,该文借助机器翻译系统,结合双语言资源的约束信息,利用标签传播算法(LP)计算词语的情感信息。在四个领域的实验结果显示我们的方法能获得一个分类精度高、覆盖领域语境的中文情感词典。     

Farsi/Arabic handwritten digit recognition using quantum neural networks and bag of visual words method

Handwritten digit recognition has long been a challenging problem in the field of optical character recognition and of great importance in industry. This paper develops a new approach for handwritten digit recognition that uses a small number of patterns for training phase. To improve performance of isolated Farsi/Arabic handwritten digit recognition, we use Bag of Visual Words (BoVW) technique to construct images feature vectors. Each visual word is described by Scale Invariant Feature Transform (SIFT) method. For learning feature vectors, Quantum Neural Networks (QNN) classifier is used. Experimental results on a very popular Farsi/Arabic handwritten digit dataset (HODA dataset) show that proposed method can achieve the highest recognition rate compared to other state of the arts methods.     

Modeling and design of printed antennas using neural networks

A single neural network is developed to model the resonant frequency of rectangular patch printed on uniaxially anisotropic substrate with air gap using effective parameters in conjunction with spectral dyadic Green's function. Also, the strength of ANN models in antenna design is demonstrated by considering two case studies: the design of circular patch antenna and planar inverted‐F antenna. Results show good agreement with literature. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2011.     

Deep learning-based optimal segmentation of 3D printed product for surface quality improvement and support structure reduction

Large-sized product cannot be printed as one piece by a 3D printer because of the volume limitation of most 3D printers. Some products with the complex structure and high surface quality should also not be printed into one piece to meet requirement of the printing quality. For increasing the surface quality and reducing support structure of 3D printed models, this paper proposes a 3D model segmentation method based on deep learning. Sub-graphs are generated by pre-segmenting 3D triangular mesh models to extract printing features. A data structure is proposed to design training data sets based on the sub-graphs with printing features of the original 3D model including surface quality, support structure and normal curvature. After training a Stacked Auto-encoder using the training set, a 3D model is pre-segmented to build an application set by the sub-graph data structure. The application set is applied by the trained deep-learning system to generate hidden features. An Affinity Propagation clustering method is introduced in combining hidden features and geometric information of the application set to segment a product model into several parts. In the case study, samples of 3D models are segmented by the proposed method, and then printed using a 3D printer for validating the performance.