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Being able to search for words or phrases in historic handwritten documents is of paramount importance when preserving cultural heritage. Storing scanned pages of written text can save the information from degradation, but it does not make the textual information readily available. Automatic keyword spotting systems
To bridge the semantic gap between low-level visual features and high-level semantic concepts, this paper puts forward a novel feedback mechanism which is based on both instance and keyword features. In offline part, keyword space model is first constructed and updated using manifold ranking annotation; in online
models for categories specified simply by their names. We show that multiple-instance learning enables the recovery of robust category models from images returned by keyword-based search engines. By incorporating constraints that reflect the expected sparsity of true positive examples into a large-margin objective function
retrieval scheme based on annotation keywords and visual content, which can benefit from the strength of text- and content-based retrieval. The system starts query triggered by some keywords, and further refines the retrieval result based on blobs and regions information. The first step is to complete semantic filtering with
In this paper, we propose a novel multi-label image annotation for image retrieval based on annotated keywords. For multi-label image annotation, a bi-coded genetic algorithm is employed to select optimal feature subsets and corresponding optimal weights for every one vs. one SVM classifiers. After an unlabelled image
Automatic image annotation is crucial for keyword-based image retrieval. There is a trend focusing on utilization of machine learning techniques, which learn statistical models from annotated images and apply them to generate annotations for unseen images. In this paper we propose MAGMA - new image auto-annotation
In this paper, we describe the use of a Boosting algorithm, Real AdaBoost, for content-based image retrieval (CBIR) on a large number (190) of keyword categories. Previous work with Boosting for image orientation detection has involved only a few categories, such as a simple outdoor vs. indoor scene dichotomy. Other
With the large number of Web sites promoting the use of illicit drugs, it has become important to screen these sites for the protection of children on the Internet. Conventional keyword-based approaches are not sufficient because these Web sites often have lots of images and little meaningful words than prices. We
integrating both low level-visual features and high-level textual keywords. Unfortunately, manual image annotation is a tedious process and may not be possible for large image databases. To overcome this limitation, several approaches that can annotate images in a semi-supervised or unsupervised way have emerged. In this paper
Content-based image retrieval systems can automatically extract visual content of images which allow users to query images by their low-level features (such as color and texture). However, users usually prefer querying images based on high-level concepts such as keywords. Classifying images into a number of categories
Image identification of plant leaves based on human vision is difficult task as well as plant identification based on keywords retrieval. It requires the domain knowledge in the botanist field. This work proposes the image texture analysis using Discrete Wavelet Transformation (DWT) and combined with an entropy
In order to enable more effective image retrieval via keywords, automatic image annotation and categorization becomes an important problem in computer vision and content based image retrieval. Unfortunately, there exists a semantic gap between the low-level feature vectors and the high-level semantics or concepts. In
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