Content-based Features Research Articles

Image semantics discovery from web pages for semantic-based image retrieval using self-organizing maps

Traditional content-based image retrieval (CBIR) systems often fail to meet a user’s need due to the ‘semantic gap’ between the extracted features of the systems and the user’s query. The cause of the semantic gap is the failure of extracting real semantics from an image and the query. To extract semantics of images, however, is a difficult task. Most existing techniques apply some predefined semantic categories and assign the images to appropriate categories through some learning processes. Nevertheless, these techniques always need human intervention and rely on content-based features. In this paper we propose a novel approach to bridge the semantic gap which is the major deficiency of CBIR systems. We conquer the deficiency by extracting semantics of an image from the environmental texts around it. Since an image generally co-exists with accompanying texts in various formats, we may rely on such environmental texts to discover the semantics of the image. We apply a text mining process, which adopts the self-organizing map (SOM) learning algorithm as a kernel, on the environmental texts of an image to extract the semantic information from this image. Some implicit semantic information of the images can be discovered after the text mining process. We also define a semantic relevance measure to achieve the semantic-based image retrieval task. We performed experiments on a set of images which are collected from web pages and obtained promising results.

WSGB

Growing Book refers to an electronic textbook that is co-developed, and has the ability to be constantly maintained, by groups of independent authors, thus creating a rich and ever-growing learning environment that can be conveniently accessible from anywhere. This work designs and implements a Web Service-based Growing Book that has the merits of single sign-in, multilevel usage, multilingual access, and multimodal multimedia for content sharing. XML Web service techniques and authentication mechanisms are used to provide single sign-in service across distributed Web sites, allowing easy teaching material reusing. Also, a multilingual page switching mechanism for Growing Books is developed and implemented. Challenges during the development of Growing Books are evaluated and analyzed. In addition to the importance of the single sign-in function, our analysis results demonstrate that the content-based features of Growing Book appeal more to students than other features and are often used for immediate judgment of course materials. Furthermore, a multilingual Growing Book is good for worldwide usage and greater accessibility.

Evaluation and analysis of similarity measures for content-based visual information retrieval

The selection of appropriate proximity measures is one of the crucial success factors of content-based visual information retrieval. In this area of research, proximity measures are used to estimate the similarity of media objects by the distance of feature vectors. The research focus of this work is the identification of proximity measures that perform better than the usual choices (e.g., Minkowski metrics). We evaluate a catalogue of 37 measures that are selected from various areas (psychology, sociology, economics, etc.). The evaluation is based on content-based MPEG-7 descriptions of carefully selected media collections. Unfortunately, some proximity measures are only defined on predicates (e.g., most psychological measures). One major contribution of this paper is a model that allows for the application of such measures on continuous feature data. The evaluation results uncover proximity measures that perform better than others on content-based features. Some predicate-based measures clearly outperform the frequently used distance norms. Eventually, the discussion of the evaluation leads to a catalogue of mathematical terms of successful retrieval and browsing measures.

An adaptive technique for content-based image retrieval

We discuss an adaptive approach towards Content-Based Image Retrieval. It is based on the Ostensive Model of developing information needs—a special kind of relevance feedback model that learns from implicit user feedback and adds a temporal notion to relevance. The ostensive approach supports content-assisted browsing through visualising the interaction by adding user-selected images to a browsing path, which ends with a set of system recommendations. The suggestions are based on an adaptive query learning scheme, in which the query is learnt from previously selected images. Our approach is an adaptation of the original Ostensive Model based on textual features only, to include content-based features to characterise images. In the proposed scheme textual and colour features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, work-task oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. This is due to its ability to adapt to the user's need, its intuitiveness and the fluid way in which it operates. Studying and comparing the nature of the underlying information need, it emerges that our approach elicits changes in the user's need based on the interaction, and is successful in adapting the retrieval to match the changes. In addition, a preliminary study of the retrieval performance of the ostensive relevance feedback scheme shows that it can outperform a standard relevance feedback strategy in terms of image recall in category search.

Law & Psychiatry: Legal Concerns for Psychiatrists Who Maintain Web Sites

Law & Psychiatry: Legal Concerns for Psychiatrists Who Maintain Web Sites

MPEG-7 시각 기술자와 해마 신경망을 이용한 내용기반 검색

As development of digital technology, many kinds of multimedia data are used variously and requirements for effective use by user are increasing. In order to transfer information fast and precisely what user wants, effective retrieval method is required. As existing multimedia data are impossible to apply the MPEG-1, MPEG-2 and MPEG-4 technologies which are aimed at compression, store and transmission. So MPEG-7 is introduced as a new technology for effective management and retrieval of multimedia data. In this paper, we extract content-based features using color descriptor among the MPEG-7 standardization visual descriptor, and reduce feature data applying PCA(Principal Components Analysis) technique. We model the cerebral cortex and hippocampal neural network in engineering domain, and team content-based feature vectors fast and apply the hippocampal neural network algorithm to compose of optimized feature. And then we present fast and precise retrieval effect when indexing and retrieving.

Automated Video Segmentation for Lecture Videos

Video, a rich information source, is commonly used for capturing and sharing knowledge in learning systems. However, the unstructured and linear features of video introduce difficulties for end users in accessing the knowledge captured in videos. To extract the knowledge structures hidden in a lengthy, multi-topic lecture video and thus make it easily accessible, we need to first segment the video into shorter clips by topic. Because of the high cost of manual segmentation, automated segmentation is highly desired. However, current automated video segmentation methods mainly rely on scene and shot change detection, which are not suitable for lecture videos with few scene/shot changes and unclear topic boundaries. In this article we investigate a new video segmentation approach with high performance on this special type of video: lecture videos. This approach uses natural language processing techniques such as noun phrases extraction, and utilizes lexical knowledge sources such as WordNet. Multiple linguistic-based segmentation features are used, including content-based features such as noun phrases and discourse-based features such as cue phrases. Our evaluation results indicate that the noun phrases feature is salient.

Time-invariant and time-varying multirate filter banks : application to image coding

This paper reviews various concepts and solutions of time-invariant and time-varying multirate filter banks. It discusses their performance for image and video coding at low bit rates, and their applicability in the mpeg-4 framework. Time-invariant multirate filter banks, and methods of design with different criteria appropriate for signal compression are first presented. Several procedures of quantization, namely scalar and lattice vector quantization, with bit allocation optimized in the rate-distortion sense, are used for the encoding of the subband signals. A technique of rate-constrained lattice vector quantization (rc-lvq), combined with a three components entropy coding, allow, together with distortion psychovi-sual weighting mechanisms to obtain significant visual improvements versus scalar quantization or the zerotree technique. However, time-invariant multirate filter banks, although efficient in terms of compression, are not well suited for content-based functionalities. Content-based features may require the ability to manipulate and thus encode a given region in the scene independently of the neighbouring regions, hence the use of transformations that can be adapted to arbitrary size bounded supports. Also, to increase the compression efficiency, one may want to adapt the transformation to the region characteristics, and thus use transform switching mechanisms, with soft or hard transitions. Three main classes of transformations can address these problems: shape-adaptive block transforms, transforms relying on signal extensions and transforms relying on time-varying multirate filter banks. These various solutions, with their methods of design, are reviewed. Emphasis is put on an extension of the SDF (symmetric delay factorization) technique which opens new perspectives in the design of time-bounded and time-varying filter banks. A region-adapted rate-distortion quantization algorithm has been used in the evaluation of the transformations compression efficiency. The coding results illustrate the interest of these techniques for compression but also for features such as quality scalability applied to selected regions of the image.

Improving World Wide Web educational uses promoting hypertext and standard general markup language content-based features

Behind the characteristics that make use of the WWW easy and attractive lie the hypertext and standard markup languages (HTML--SGML), metalanguages for defining the syntax of markup languages. The fundamental principle that makes it possible to share information through the WWW with independence of environment, is based on the standard encoding of documents. Most current educational applications do not fully exploit the flexibility offered by what lies at the core of the WWW, the language HTML. A discussion of different taxonomies of educational uses of the WWW is followed by the presentation of an alternative taxonomy that aims to reflect the contribution of HTML. This is a double classification that distinguishes between the educational context, where the innovation brought by the WWW is mainly technological, and new contexts promoted by the WWW that could not exist without it. From this it is possible to envisage two lines of future work. First to analyse the educational possibilities offered by the current definition of HTML and to propose ways to use them in particular applications. Second, to provide the specifications and requirements to develop instructional tools to make use of the educational possibilities of the language. An example of the latter is provided by the developmental work being undertaken to provide language support as part of a Socrates--Lingua project. This has led to the conclusion that the present definition of HTML is insufficient for the organization of all the data required in educational applications. Instead an extended markup using SGML is recommended as the way forward for designers