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 A Retrieval Mechanism for Multi-versioned Digital Collection Using TAG

 A Retrieval Mechanism for Multi-versioned Digital Collection Using TAG

Abstract— As the marvellous growth of the digital library in each year, the problems with indexing and searching a digital library is increased in a high rate. When the researchers search for the earlier versions, only a few recent versions in the back volumes can be retrieved soon. It is unpredictable that researchers require the earlier versions in a specific boundary. In order to facilitate the researchers, who may access any version at any time, we propose a VTAG technique for indexing. Our experiments indicate that the proposed retrieval technique, VTAG, effectively retrieves any version in considerable amount of time than the existing method.
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Knn And Steerable Pyramid Based Enhanced Content Based Image Retrieval Mechanism

Knn And Steerable Pyramid Based Enhanced Content Based Image Retrieval Mechanism

Recently, user’s relevance feedback is also, incorporated to further improve the retrieval process in order to produce perceptually and semantically more meaningful retrieval results. In this chapter, we discuss these fundamental techniques for content- based image retrieval. CBIR is the application of computer vision to aid the image retrieval process of searching for digital images in large database based on the comparison of low level features of images. The search is carried out by using contents of the image themselves rather than relying on human-inputted metadata such as caption or keyword describing the image. Compared to text- based retrieval systems, CBIR is more feasible in large-scale databases and is usually used in environments which require fast retrieval and real-time operations. Software’s which implements CBIR are known as content-based image retrieval systems (CBIRS). CBIR came to the interest of researchers as it offers the ability to index images based on content of the image itself [4]. In a standard CBIR based machine (Figure 1), image based features like color, texture, shape of an image and spatial locations are shown and represented in the form of a multidimensional feature vector. The characteristic vectors of images inside the database form a feature database. The retrieval process in CBIR system is started whenever a consumer question the system using query image or provides the sketch of the image [3]. The question photograph or the query image is converted into the internal illustration of feature vector using the equal characteristic extraction process that was used for constructing the feature database. The similarity measure or the degree is hired to calculate the distance among the feature vectors of query image and those of the target images inside the characteristic database of images. Finally, the retrieval is achieved by using an indexing scheme which facilitates the efficient searching of the image database. Recently, consumer’s relevance remarks or we can say the feedback is likewise included to further enhance the retrieval method so one can produce perceptually and semantically greater meaningful retrieval effects using CBIR system [4].CBIR retrieves images based on visual features such as colour, texture and shape [3]. In this method, colour, shape and texture of an image are classified automatically or semi-automatically with the aid of human classifier. Retrieval results are obtained by calculating the similarity between the query and images stored in the database using predefined distance measure. The results are than ranked according to the highest similarity score.
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National University of Sciences & Technology

National University of Sciences & Technology

Web100 Server Control Mechanism Windows Data Retrieval Mechanism Linux Data Retrieval Mechanism One of these is selected TCP Session Monitoring Little MonALISA LML GUI LML Repositor[r]

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Query Word Image based Retrieval Scheme for Handwritten Tamil Documents

Query Word Image based Retrieval Scheme for Handwritten Tamil Documents

An adaptive run length smoothing algorithm has been used to handle the problem of complex and dense document layout in the segmentation of document pages resulting from the digitiza- tion of historical machine printed sources [7]. A technique for keyword guided word spotting in historical printed documents has been suggested to initialize the word retrieval mechanism through the creation of synthetic word data combined with hy- brid feature extraction [5]. A system for recognizing handwritten manuscripts based on hidden Markov models supported by a lan- guage model has been presented. This system has been found to investigate two approaches, one that involves training the rec- ognizer from scratch, and the other that adapts it from a recog- nizer trained on a general offline handwriting database [3]. A document image retrieval system that performs word matching directly in the document images has been developed and evalu- ated on a collection of documents created from various texts with noise added in them [14]. The experimental results for Tamil online handwritten character recognition using HMM and sta- tistical dynamic time warping (SDTW) as classifiers have been reported [4].
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Autonomic management in a distributed storage system

Autonomic management in a distributed storage system

Another facet of a distributed storage system whose performance and resource consumption depends on a specific configuration parameter is the data retrieval mechanism. This con- figuration parameter is the degree of concurrency (DOC) with which data is retrieved. The DOC controls the eagerness with which multiple redundant replicas are retrieved. Again, two unsatisfactory situations can be identified. The first arises when the DOC is low and there is a large variation in the times taken to retrieve replicas. In this situation it is desir- able to increase the DOC, because by retrieving more replicas in parallel a result can be returned to the user sooner. The converse situation arises when the DOC is high, there is lit- tle variation in retrieval time and there is a network bottleneck close to the requesting client. In this situation it is desirable to decrease the DOC, since the low variation removes any benefit in parallel retrieval, and the bottleneck means that decreasing parallelism reduces both bandwidth consumption and elapsed time for the user.
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Using Random Walks for Question focused Sentence Retrieval

Using Random Walks for Question focused Sentence Retrieval

As shown in Table 7, LR[0.20,0.95] outperformed the baseline system on the test data both in terms of average MRR and TRDR scores. The improve- ment in average TRDR score was statistically sig- nificant with a p-value of 0.0619. Since we are in- terested in a passage retrieval mechanism that finds sentences relevant to a given question, providing in- put to the question answering component of our sys- tem, the improvement in average TRDR score is very promising. While we saw in Section 5.1 that LR[0.20,0.95] may perform better on some question or cluster types than others, we conclude that it beats the competitive baseline when one is looking to op- timize mean TRDR scores over a large set of ques- tions. However, in future work, we will continue to improve the performance, perhaps by develop- ing mixed strategies using different configurations of LexRank.
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Applying the Semantic Web to Manage Knowledge on the Grid

Applying the Semantic Web to Manage Knowledge on the Grid

Abstract: Geodise [2] uses a toolbox of Grid enabled Matlab functions as building blocks on which higher-level problem solving workflows can be built. The aim is to help domain engineers utilize the Grid and engineering design search packages to yield optimized designs more efficiently. In order to capture the knowledge needed to describe the functions & workflows so that they may be best reused by other less experienced engineers we have developed a layered semantic infrastructure. A generic knowledge development and management environment (OntoView) that is used to develop an ontology encapsulating the semantics of the functions and workflows, and that underpins the domain specific components. These include: an annotation mechanism used to associate concepts with functions (Function Annotator); a semantic retrieval mechanism and GUI that allows engineers to locate suitable functions based on a list of ontology-driven searching criteria; and a GUI-based function advisor that uses the functions’ semantic information in order to help function configuration and recommend semantically compatible candidates for function assembly and workflow composition (Domain Script Editor and Workflow Construction Advisor). This paper describes this infrastructure, which we plan to extend to include the semantic reuse of workflows as well as functions.
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ERP correlates of forgetting: an investigation of resource allocation as a potential neural mechanism behind retrieval induced forgetting

ERP correlates of forgetting: an investigation of resource allocation as a potential neural mechanism behind retrieval induced forgetting

think. Other studies have found that RIF effects are insensitive to age (Hogge, Adam, & Collette, 2008), and illnesses that have been known to affect inhibitory control (Moulin et al., 2002). Even amongst healthy young adults whose inhibition we would to be intact, RIF effects are variable. In Johansson et al.’s study (2007), authors conducted a post-hoc analysis by diving their participants into a “high-forgetting” and “low-forgetting” group. While both groups demonstrated significant facilitation effects, the low forgetting group did not produce a significant retrieval-induced forgetting effect. The participants were of the same age group and underwent the same experimental paradigm. How can we account for this variation amongst individuals? Why do some people seem vulnerable to RIF effects and others not? Whatever the case may be, the absence of RIF in the present study demonstrates how much we still have much to learn about the phenomenon and that it may not be as simple or generalizable as it seems.
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Content based analysis retrieval using audiovisual archive retrieval  with cross referencing and multimodal re ranking

Content based analysis retrieval using audiovisual archive retrieval with cross referencing and multimodal re ranking

Content-based video retrieval (CBVR) (Huurnink et al., 2010) is the technique for audio-visual retrieval problem; it is useful for searching videos in large databases. Content based analysis gives video indexing (VI) for effective audio visual archiving and retrieving. It is a source for media professionals in different field to archive video from database for reusage. Content based analysis provides a solution for the inevitably tedious and incomplete video fragments archive. Fine grained manual and automatic annotation source are helpful for the users to retrieve exact data. Common initial steps for most content based video analysis techniques are to segment a video into elementary shots using video extractor. Based on the description and similarity between the shots result is obtained by query given by the user. Some query input methods are text, image and even video as query for audio visual archive. In images query, the histogram approaches and video query is by shot-by-shot detection. Textual query is the most common query used in the search system to find the relevant data based on the description given in query interface. In this author (Huurnink et al., 2010), the media professionals are finding a new material for audio-visual archive. In cross referencing is introduced here for finding the combination of the similar audio-visual archived material for retrieval process. Finally, multimodal re-ranking is used to ranking the retrieved audio- visual material into the cluster level.
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A Comparative Study of Image Retrieval Algorithms for Enhancing a Content-Based Image Retrieval System

A Comparative Study of Image Retrieval Algorithms for Enhancing a Content-Based Image Retrieval System

These algorithms rely on extract a signature for every image based on its pixel values, and to define a rule for comparing images. However, only the color signature is used as a signature to retrieve images. Existing color based general-purpose image retrieval systems roughly fall into three categories depending on the signature extraction approach used: histogram, color layout, and region-based search. And histogram- based search methods are investigated in two different color spaces. A color space is defined as a model for representing color in terms of intensity values. Typically, a color space defines a one-to four-dimensional space. Three-dimensional color spaces such, RGB (Red, Green, and Blue) and HSV (Hue, Saturation and Value), are investigated [12].
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Learning Robust Visual-Semantic Retrieval Models with Limited Supervision

Learning Robust Visual-Semantic Retrieval Models with Limited Supervision

On the other hand, streams of images with noisy tags are readily available in datasets, such as Flickr-1M [54], as well as in nearly infinite numbers on the web. Developing a practical system for image-text retrieval considering a large number of web images is more likely to be robust. However, inefficient utilization of weakly-annotated images may increase ambiguity and degrade performance. Motivated by this observation, we pose an important question: Can a large number of web images with noisy annotations be leveraged upon with a fully annotated dataset of images with textual descriptions to learn better joint embeddings? Fig. 4.2 shows an illustration of this scenario. This is an extremely relevant problem to address due to the difficulty and non-scalability of obtaining a large amount of human-annotated training set of image-text pairs. In this work, we study how to judiciously utilize web images to develop a successful image-text retrieval system. We propose a novel framework that can augment any ranking loss based supervised formulation with weakly- supervised web data for learning robust joint embeddings.
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CBIR using SIFT& FDCT with Relevance Feedback Mechanism

CBIR using SIFT& FDCT with Relevance Feedback Mechanism

Content-based image retrieval (cbir) is the application of laptop imaginative and prescient to the photograph retrieval trouble, i.e. attempting to find digital pictures from huge databases. content material based image retrieval makes use of shade, texture and form features [1] .a machine which can filter out snap shots based totally on their content could offer better indexing and return extra correct effects. in content material based totally commonly image retrieval (cbir),[2] images place unit indexed by way of their visual content like color, texture and form. Those Low-level image alternatives region unit inadequate to explain most internet based generally picture search engines like google agree with strictly on facts and this produces heaps of garbage in the outcomes. Many structures had been developed but now not a unmarried gadget is perfect. green control of the rapidly expanding visual records is wanted. to search the most similar photographs to the question photograph, by way of fast discrete curvelet transforms [3] for better retrieval effects.
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Applying Feedback Mechanism for Content Based Image Retrieval Using Data Mining Technique

Applying Feedback Mechanism for Content Based Image Retrieval Using Data Mining Technique

42 Content-based image retrieval uses the illustration contents of an image such as color, shape, surface, and spatial arrangement to represent and index the image. In typical local histogram based system (Figure 1-1), the visual contents of the images in the database are remove and describe by multi-dimensional characteristic vectors e.g. [5] . The attribute vectors of the images in the database form a characteristic database. To retrieve images, users provide the retrieval system with example images or sketch figures. The system then changes these examples into its internal representation of characteristic vectors. The related ity distance between the characteristic vectors of the query example or drawing and those of the images in the database are then calculated and taking back is performed with the aid of an indexing visual information, are on the increase worldwide. The goal of this project is to travel around important enabling techniques for an image retrieval system to lay a solid method. The indexing method provides a well-organized way to search for the image database. Recent retrieval systems have incorporated users' relevance feedback to modify the taking back process in order to generate perceptually and semantically more important retrieval results. In this chapter, we introduce these essential techniques for content-based image retrieval. image (in the form of a histogram or probability distribution depicting the intensities of pixels in an image) is the most widely used characteristic for content-based image retrieval (CBIR), e.g. [12] while texture and shape characteristics are also used, albeit to a smaller degree. The three types of image characteristics are utilized in different CBIR applications ranging from scene/object and fingerprint categorization and matching to face and pattern identification. More frequentlythan not, a single characteristic is not enough to seperate among a homogeneous group of images. In such cases, either pairs of these characteristics or all of them are used for the purpose of indexing and retrieval. The advance in multimedia content have fashioned an massive number of digital imagery files in a mixture of application domains. All this imagery in sequence is useful only when one can access it well-organizedly.
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Thesaurus based Efficient Example Retrieval by Generating Retrieval Queries from Similarities

Thesaurus based Efficient Example Retrieval by Generating Retrieval Queries from Similarities

Thesaurus based Efficient Example Retrieval by Generating Retrieval Queries from Similarities hesaurus based Efficient Example Retrieval by Generating Retrieval Queries from Similarities* Takehito Uts[.]

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Information Retrieval: A Survey - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials

Information Retrieval: A Survey - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials

“Clustering” of documents is the grouping of documents into distinct classes according to their intrinsic (usually statistical) properties. Clustering is a kind of classification but it differs from the classification for routing purposes discussed in the section above on routing in one crucial respect: In a routing application, the documents are classified in terms of their similarity or relevance to external queries or topics or user profiles. In “clustering,” we seek features that will separate the documents into natural groups based entirely on the internal properties of the collection. Ideally, the groups will be completely separate and as far apart as possible in feature space. But some- times, overlap of clusters is unavoidable. [van Rijsbergen, 1979] Since clustering depends on the statistical properties of the collection being clustered rather than on matching the documents against some external set of queries, it is normally (but not always - see below!) applied to a pre- existing collection rather than an incoming stream of documents as in a routing application. Why should documents be clustered? The basic reason is that clustering can reveal the intrinsic structure of a collection, e.g., by topic, subtopic, etc., (assuming of course, that there is a signifi- cant internal structure). If a language-independent statistical method such as “n-grams” is used, a collection may also be clustered by language or document type, by topic within language, etc. (See section 3.3.6.) Moreover, by the “cluster hypothesis,” “closely associated documents tend to be relevant to the same requests.” [van Rijsbergen, 1979] Document clustering of a large collec- tion is particularly effective when it is hierarchical, i.e., when the collection is partitioned into (relatively) large, high-level clusters corresponding to broad categories, each high-level cluster in turn clustered into smaller clusters corresponding to tighter, more cohesive categories, which in turn are composed of still smaller, still more cohesive clusters, and so on. Ideally, the lowest level clusters in such a hierarchy will consist of documents that are very similar, e.g., that are all rele- vant to most of the same topics or queries. Hence, clustering, especially when combined with modern graphical display techniques, can be an effective tool for browsing a large collection and “zeroing in” on documents relevant to some given topic or other criterion. For similar reasons, it can increase the effectiveness of document retrieval, i.e., of querying large collections. [Willetts, IP&M, 1988]
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LANGUAGE MODEL FOR DIGITAL RECOURSE OBJECTS RETRIEVAL

LANGUAGE MODEL FOR DIGITAL RECOURSE OBJECTS RETRIEVAL

First proposed by Ponte and Croft [4] and described by Berger and Lafferty [49]. The basic idea behind query likelihood retrieval model is to infer a LM for each document, estimate the probability of query in document, and rank documents based on the probability of a query being generated from a document P(d|q) [50]. To estimate P(d|q) by using the Bayes rule with these assumptions: (i) P(q) is the same for all documents and (ii) P(d) is treated as uniform across all d, (vi) all words are independent. According Zhai and Lafferty [51] the query likelihood model has generalized to the Kullback-Leibler (KL) divergence scoring method, by modelling the query separately. Among many approaches of LM have proposed, the most popular and fundamental one is the query-likelihood language model, it is shown to be theoretically superior and confirmed experimentally by Bruza and Song [52], Mei, et al. [53] , Lv and Zhai [54] and Lin and Bilmes [55]. Furthermore, Cummins, et al. [56] has shown that the query likelihood model with Dirichlet smoothing can be implemented as effectively as traditional retrieval.
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A Study on Content Based Image Retrieval Systems

A Study on Content Based Image Retrieval Systems

Content-based image retrieval technique uses visual contents to search images from large scale image databases based on users’ interests. It becomes an active and fast advancing research area. Image content may include both visual and semantic content. Content-Based Image Retrieval (CBIR) is a technique for retrieving images on the basis of automatically-derived features such as color, texture and shape [2]. These techniques includes several areas such as image segmentation, image feature extraction, representation, mapping of features to semantics, storage and indexing, image similarity-distance measurement and retrieval which makes CBIR system development as a challenging task [3]. Several companies are maintaining large image databases, where the requirement is to have a technique that can search and retrieve images in a manner that is both time efficient and accurate [4].
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Lexical retrieval after Arabic aphasia: Syntactic access and predictors of spoken naming

Lexical retrieval after Arabic aphasia: Syntactic access and predictors of spoken naming

A possible interpretation of participants’ performance in the first condition is that the lexical access impairment present in the current participants reduced activation from semantics to the lemma representation of the noun in question, resulting in difficulties in the selection of the target noun and, for example in the selection of a semantically related representation, resulting in morpho-syntactic errors. However, in the second condition, access of the determiner lemma prior to the noun might have resulted in additional activation sent from the determiner lemma to different noun lemma representations. As a result, the pre-activation of noun lemmas, including the target noun lemma boosted their activation levels, so that the activation of the target noun lemma was high enough for its selection once it received semantic activation. The fact that producing a noun with a determiner led to more accurate and faster naming in Arabic is consistent with recent neuropsychological studies investigating the role of determiner in noun production (e.g. English: Gregory et al., 2010, Herbert and Best, 2010; Maltese: Ritschel, 2009). These studies found that determiners which do not inflect for syntactic information of the following noun facilitated lexical retrieval. The Arabic definite article /əl-/ ‘the’ is a neutral determiner; it does not inflect for any syntactic properties of the noun it determines, serving only as a marker of definiteness. Despite this, the definite article led to shorter latencies and greater accuracy, which challenges the assumption that determiners can only facilitate lexical retrieval if they inflect for syntactic properties of the noun they determine (e.g. Miozzo and Caramazza, 1999; Schriefers, 1993; Schriefers, Jescheniak and Hantsch, 2002). The results from the current study support the view that agreement presence is not necessary for syntax to be activated as the Arabic definite article does not inflect number or gender.
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Shape based Image Classification using Geometric ­–Properties

Shape based Image Classification using Geometric ­–Properties

Rajbhadur Yadav et al [4] have applied Fourier Descriptor (FD) and the Wavelet Descriptor (WD) technique. To find the shape feature vector value. Euclidean distance is used to measure the shape similarity. Wavelet Descriptor gives better performance compared to the Fourier Descriptor in this shape based classification they have identified in vehicular objects. In the year 2015 Cahya and Kohei [5] compared various contour extraction methods and Fourier Descriptor. EDs used to find the similarity matching between the query image and the database image. If the measured values are 0 (match to the query image) and 1(it means does not match to the query image). Shalu Gupta et al [6] have discussed various image features and they are global and local features. Feature extraction can be specified into four types of extraction such as Chord, diagonal, sub widow based, connected and nonconnected contour segment pixels. These techniques are used to extract image features. There are two classifiers used to classify the images. One is KNN and another is SVM. Finally KNN gives better accuracy than SVM. In order to identify the object similarity matching, shape based image retrieval method is used to Rehman et al [7] in the year 2016. It gives better results in CBIR Application. Wavelet Transformation is a signal analysis method and has the advantage of multiresolution analysis. Pallavi and Megha [8] is applied this technique to face recognition and found that WT gives better accuracy. ED is used to calculate the similarity between the query image and the database image. Prochazka [9] has focused on image restoration using Wavelet Transformation. Signal decomposition includes Discrete Wavelet Transformation and Discrete Fourier Transform. Two dimensional object recognition system is determined in the year 2018 by Kamlesh Kumar et al [10].WT is applied and orthogonal functions are identified. Finally low pass filter and high pass filter are applied. It is proved that WT gives better accuracy to identify objects.
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