The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In recent years, hashing techniques are becoming overwhelmingly popular for their high efficiency in handling large-scale computer vision applications. It has been shown that hashing techniques which leverage supervised information can significantly enhance performance, and thus greatly benefit visual search tasks. Typically, a modern hashing method uses a set of hash functions to compress data samples...
Automatic analysis of histopathological images has been widely investigated using computational image processing and machine learning techniques. Computer-aided diagnosis (CAD) systems and content-based image retrieval (CBIR) systems have been successfully developed for diagnosis, disease detection, and decision support in this area. In this paper, we focus on a scalable image retrieval method with...
In classical content-based image retrieval (CBIR) system, using Euclidean metric, it usually can not achieve good results, because of the semantic gap. To solve the difficulty problem, present a relevance feedback(RF) paradigm which is naturally guided only on dimension reduction with radial basis function(RBF). While images are often represented by feature vectors, the distance is usually different...
Existing Automatic Image Annotation (AIA) systems are typically developed, trained and tested using high quality, manually labelled images. The tremendous manual efforts required with an untested ability to scale and tolerate noise all have an impact on existing systems' applicability to real-world data. In this paper, we propose a novel AIA system which harnesses the collective intelligence on the...
Texture image retrieval system using contourlet transform has better performance than the same structure system based on wavelet transform due to contourlet's better directional information representation than wavelet transform. In order to improve the retrieval rate further, a dual-tree complex contourlet transform based texture image retrieval system was proposed in this paper. In the system, the...
Content-based image retrieval (CBIR) solutions with regular Euclidean metric usually cannot achieve satisfactory performance due to the semantic gap. Hence, relevance feedback has been adopted as a promising approach to improve the search performance. In this paper, we propose a novel idea of learning with historical relevance feedback log-data, and adopt a new methodology called "Collaborative...
This paper proposes a generalized Bayesian strategy for relevance feedback in Region-Based image retrieval The presented feedback technique is based on Bayesian learning method and incorporates a time-varying user model . We give the user model with two terms: a target query and a user conception. The user conception is aimed to learn a parameter set to determine the time-varying matching criterion...
In this paper, we present methodology to Content Based Image Retrieval (CBIR), focusing on developing an efficient image retrieval methodology. This scheme include: a new indexing method based on fuzzy logic to incorporate color, texture, and shape information into a region based approach to improving the retrieval effectiveness and robustness, a novel hierarchical indexing structure and the corresponding...
To solve the problem of learning a mapping function from low-level feature space to high-level semantic space, we propose a relevance feedback scheme which is naturally conducted only on the image manifold in question rather than the total ambient space. While images are typically represented by feature vectors, the natural distance is often different from the distance induced by the ambient space...
In this work, we described a new two-stage hierarchical framework for mammogram retrieval. We tested the proposed approach on the reference library from USF-DDSM. For each query ROI (region of interest), the proposed scheme first computes its 14 texture and shape features, then the voting method based on five classifiers is used to classify the ROIs in the reference library, this phase eliminates...
Latent semantic indexing (LSI), as a popular textual information retrieval approach, has been used heavily for many years. However, the use of the approach in image retrieval has been limited. In this paper, a method of using LSI in combination with the salient image representation based on a saliency-based bottom-up visual attention computational model (VACM) motivated by visual physiological experimental...
Multi-instance learning(MIL) is a new framework for learning from ambiguity, which is feasible for query-by-example(QBE) paradigm in content-based image retrieval(CBIR), since the query image posed by the user is often ambiguous and difficult to be perceived. Image bag generator, which can transform images into image bags, plays an important role in applying MIL for CBIR according to some researchers'...
Typical content-based image retrieval (CBIR) solutions with regular Euclidean metric usually cannot achieve satisfactory performance due to the semantic gap challenge. Hence, relevance feedback has been adopted as a promising approach to improve the search performance. In this paper, we propose a novel idea of learning with historical relevance feedback log data, and adopt a new paradigm called ldquoCollaborative...
Salient region of the image, which is composed of salient or interest points, is the most informative part of the image. In this paper, a saliency-based bottom-up visual attention computational model motivated by visual physiological experimental results is used to detect salient region and extract salient points of images. Meanwhile, a method to select number of the salient points to be extracted...
Since texture describes the local information of pixels' intensity variation, which can be regarded as the non-linear signals, non-linear signal analysis methods may be applied to texture analysis. Complexity analysis, as a popular non-linear signal analysis approach, is widely used for biological and clinical data analysis. In this paper, for exploring study purpose, a two-dimensional structure complexity...
Recently, various features for content-based image retrieval (CBIR) have been proposed, such as texture, color, shape, and spatial features. In this paper we propose a new feature, called orientation-distance histogram for CBIR. Firstly, we transform the RGB color model of a given image to the HSI color model and detect edge points by using the H-vector information. Secondly, we evaluate the orientation-distance...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.