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.     

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.     

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.     

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.     

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.     

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.     

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.     

Facial expression classification using salient pattern driven integrated geometric and textual features

Multimedia Tools and Applications - Facial expression classification aims to recognize human emotion via face images. The major challenge of facial expression classification is how to extract...     

Picture indexing and abstraction techniques for pictorial databases

We present an approach for picture indexing and abstraction. Picture indexing facilitates information retrieval from a pictorial database consisting of picture objects and picture relations. To construct picture indexes, abstraction operations to perform picture object clustering and classification are formulated. To substantiate the abstraction operations, we also formalize syntactic abstraction rules and semantic abstraction rules. We then illustrate by examples how to apply these abstraction operations to obtain various picture indexes, and how to construct icons to facilitate accessing of pictorial data.     

Adaptive face representation via class-specific and intra-class variation dictionaries for recognition

Face recognition has attracted extensive interests due to its wide applications. However, there are many challenges in the real world scenario. For example, relatively few samples are available for training. Face images collected from surveillance cameras may consist of complex variations (e.g. illumination, expression, occlusion and pose). To address these challenges, in this paper we propose learning class-specific and intra-class variation dictionaries separately. Specifically, we first develop a discriminative class-specific dictionary amplifying the differences between training classes. We impose a constraint on sparse coefficients, which guarantees the sparse representation coefficients having small within-class scatter and large between-class scatter. Moreover, we introduce a new intra-class variation dictionary based on the assumption that similar variations from different classes may share some common features. The intra-class variation dictionary not only captures the inner-relationship of variations, but also addresses the limitation of the manually designed dictionaries that are person-specific. Finally, we apply the combined dictionary to adaptively represent face images. Experiments conducted on the AR, CMU-PIE, FERET and Extended Yale B databases show the effectiveness of the proposed method.     

Sentiment analysis in textual,visual and multimodal inputs using recurrent neural networks

Social networking platforms have witnessed tremendous growth of textual, visual, audio, and mix-mode contents for expressing the views or opinions. Henceforth, Sentiment Analysis (SA) and Emotion Detection (ED) of various social networking posts, blogs, and conversation are very useful and informative for mining the right opinions on different issues, entities, or aspects. The various statistical and probabilistic models based on lexical and machine learning approaches have been employed for these tasks. The emphasis was given to the improvement in the contemporary tools, techniques, models, and approaches, are reflected in majority of the literature. With the recent developments in deep neural networks, various deep learning models are being heavily experimented for the accuracy enhancement in the aforementioned tasks. Recurrent Neural Network (RNN) and its architectural variants such as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) comprise an important category of deep neural networks, basically adapted for features extraction in the temporal and sequential inputs. Input to SA and related tasks may be visual, textual, audio, or any combination of these, consisting of an inherent sequentially, we critically investigate the role of sequential deep neural networks in sentiment analysis of multimodal data. Specifically, we present an extensive review over the applicability, challenges, issues, and approaches for textual, visual, and multimodal SA using RNN and its architectural variants.

     

Visual and textual consistency checking tools for graphical userinterfaces

Designing user interfaces with consistent visual and textual properties is difficult. To demonstrate the harmful effects of inconsistency, we conducted an experiment with 60 subjects. Inconsistent interface terminology slowed user performance by 10 to 25 percent. Unfortunately, contemporary software tools provide only modest support for consistency control. Therefore, we developed SHERLOCK, a family of consistency analysis tools, which evaluates visual and textual properties of user interfaces. It provides graphical analysis tools such as a dialog box summary table that presents a compact overview of visual properties of all dialog boxes. SHERLOCK provides terminology analysis tools including an interface concordance, an interface spellchecker, and terminology baskets to check for inconsistent use of familiar groups of terms. Button analysis tools include a button concordance and a button layout table to detect variant capitalization, distinct typefaces, distinct colors, variant button sizes, and inconsistent button placements. We describe the design, software architecture, and the use of SHERLOCK. We tested SHERLOCK with four commercial prototypes. The outputs, analysis, and feedback from designers of the applications are presented     

Image registration for automated inspection of printed circuit patterns using CAD reference data

An image registration approach for inspection of printed circuit patterns which has been validated on a prototype system is described. Theoffline procedure forms, selects, prioritizes, and sorts registration features from CAD-generated reference data. A feature is selected if it satisfies clearance rules that account for the maximum expecteddiscongruence between captured and reference images. The sorting scheme considers the detection complexity of a feature and its distance away from the center of the expected image, since outer features represent potential global distortions better. Theruntime registration procedure detects features and finds the parameters that transform pixels into reference data coordinates and vice versa. We represent robust feature-measurement techniques that offer accurate subpixel localization and verify feature authenticity. We describe an edge-detection technique based on a novel way of authenticating zero-crossings and a method that disqualifies edges detected on defects of the part under inspection.     

A practical design procedure for circularly polarized printed arrays using high frequency CAE

A practical design procedure for a circularly polarized printed array antenna composed of strip dipoles and slots (CP-PASS) is presented. CP-PASS is a kind of series-fed array, and the equivalent circuit model of CP-PASS is simplified to be suitable for a computer-aided design (CAD) package. Some experimental results of a new strip element excited by a stripline and a 9-element set CP-PASS are also presented.     

Visual and textual fusion for semantically supervised region-based retrieval

This paper presents a unified annotation and retrieval framework, which integrates region annotation with image retrieval for performance reinforcement. To integrate semantic annotation with region-based image retrieval, visual and textual fusion is proposed for both soft matching and Bayesian probabilistic formulations. To address sample insufficiency and sample asymmetry in the annotation classifier training phase, we present a region-level multi-label image annotation scheme based on pair-wise coupling support vector machine (SVM) learning. In the retrieval phase, to achieve semantic-level region matching we present a novel retrieval scheme which differs from former work: the query example uploaded by users is automatically annotated online, and the user can judge its annotation quality. Based on the user’s judgment, two novel schemes are deployed for semantic retrieval: (1) if the user judges the photo to be well annotated, Semantically supervised Integrated Region Matching is adopted, which is a keyword-integrated soft region matching method; (2) If the user judges the photo to be poorly annotated, Keyword Integrated Bayesian Reasoning is adopted, which is a natural integration of a Visual Dictionary in online content-based search. In the relevance feedback phase, we conduct both visual and textual learning to capture the user’s retrieval target. Better annotation and retrieval performance than current methods were reported on both COREL 10,000 and Flickr web image database (25,000 images), which demonstrated the effectiveness of our proposed framework.