16359335
Scalable relevance feedback using click-through data for web image retrieval
Relevance feedback (RF) has been extensively studied in the content-based image retrieval community. However, no commercial Web image search engines support RF because of scalability, efficiency and effectiveness issues. In this paper we proposed a scalable relevance feedback mechanism using click-through data for web image retrieval. The proposed mechanism regards users' click-through data as implicit feedback which could be collected at lower cost, in larger quantities and without extra burden on the user. During RF process, both textual feature and visual feature are used in a sequential way. To seamlessly combine textual feature-based RF and visual feature-based RF, a query concept-dependent fusion strategy is automatically learned. Experimental results on a database consisting of nearly three million Web images show that the proposed mechanism is wieldy, scalable and effective.
18096926
Neighborhood Preserving Projections (NPP): A Novel Linear Dimension Reduction Method
Dimension reduction is a crucial step for pattern recognition and information retrieval tasks to overcome the curse of dimensionality. In this paper a novel unsupervised linear dimension reduction method, Neighborhood Preserving Projections (NPP), is proposed. In contrast to traditional linear dimension reduction method, such as principal component analysis (PCA), the proposed method has good neighborhood-preserving property. The main idea of NPP is to approximate the classical locally linear embedding (i.e. LLE) by introducing a linear transform matrix. The transform matrix is obtained by optimizing a certain objective function. Preliminary experimental results on known manifold data show the effectiveness of the proposed method.
140083992
Boosting image classification with LDA-based feature combination for digital photograph management
Image classification is of great importance for digital photograph management. In this paper we propose a general statistical learning method based on boosting algorithm to perform image classification for photograph annotation and management. The proposed method employs both features extracted from image content (i.e., color moment and edge direction histogram) and features from the EXIF metadata recorded by digital cameras. To fully utilize potential feature correlations and improve the classification accuracy, feature combination is needed. We incorporate linear discriminant analysis (LDA) algorithm to implement linear combinations between selected features and generate new combined features. The combined features are used along with the original features in boosting algorithm for improving classification performance. To make the proposed learning algorithm more efficient, we present two heuristics for selective feature combinations, which can significantly reduce training computation without losing performance. The proposed image classification method has several advantages: small model size, computational efficiency and improved classification performance based on LDA feature combination.
2157887
Concurrent subspaces analysis
A representative subspace is significant for image analysis, while the corresponding techniques often suffer from the curse of dimensionality dilemma. In this paper, we propose a new algorithm, called concurrent subspaces analysis (CSA), to derive representative subspaces by encoding image objects as 2/sup nd/ or even higher order tensors. In CSA, an original higher dimensional tensor is transformed into a lower dimensional one using multiple concurrent subspaces that characterize the most representative information of different dimensions, respectively. Moreover, an efficient procedure is provided to learn these subspaces in an iterative manner. As analyzed in this paper, each sub-step of CSA takes the column vectors of the matrices, which are acquired from the k-mode unfolding of the tensors, as the new objects to be analyzed, thus the curse of dimensionality dilemma can be effectively avoided. The extensive experiments on the 3/sup rd/ order tensor data, simulated video sequences and Gabor filtered digital number image database show that CSA outperforms principal component analysis in terms of both reconstruction and classification capability.
16350167
Efficient propagation for face annotation in family albums
In this paper, we propose and investigate a new user scenario for face annotation, in which users are allowed to multi-select a group of otogras and assign names to these otogras. The system will then attempt to propagate names from otogra level to face level, i.e. to infer the correspondence between name and face. Given the face similarity measure which combines methodologies from face recognition and content-based image retrieval, we formulate name propagation as an optimization problem. We define the objective function as the sum of similarities between each pair of faces of the same individual in different otogras, and propose an iterative optimization algorithm to infer the optimal correspondence. To make the propagation result reliable, a reject scheme is adopted to reject those with low confidence scores. Furthermore, we investigate the combination and alternation of browsing mode for propagation and viewer mode for annotation, so that each mode can benefit from additional inputs from the other mode. The experimental evaluation has been conducted within a typical family album of over one thousand otogras and the results show that the proposed approach is effective and efficient in automated face annotation in family albums.
5415099
Face Annotation for Family Photo Album Management
In this paper, we propose a framework to semi-automatically annotate faces in family photo albums. The core of the framework is the features used to define face similarity and this results in the learning algorithm used to refine automatic face annotation. We have adopted similarity based search and relevance feedback ideas developed for content-based image retrieval and a set of simple yet effective color and texture based features, in addition to the traditional face recognition features, in performing candidate annotation search. The experimental evaluation of the proposed approach has been conducted with a family album of 1707 photos and the results show that the proposed approach is an effective and efficient one for semi-automatic family photo album annotation.
5430292
A practical evaluation of kinetic data structures
In many applications of computational geometry to modeling objects and processes in the physical world, the participating objects are in a state of continuous change. Motion is the most ubiquitous kind of continuous transformation but others, such as shape deformation, are also possible. In a recent paper, Baech, Guibas, and Hershberger [BGH97] proposed the framework of kinetic data structures (KDSS) as a way to maintain, in a completely on-line fashion, desirable information about the state of a geometric system in continuous motion or change. They gave examples of kinetic data structures for the maximum of a set of (changing) numbers, and for the convex hull and closest pair of a set of (moving) points in the plane. The KDS frameworkallowseach object to change its motion at will according to interactions with other moving objects, the environment, etc. We implemented the KDSSdescribed in [BGH97],es well as came alternative methods serving the same purpose, as a way to validate the kinetic data structures framework in practice. In this note, we report some preliminary results on the maintenance of the convex hull, describe the experimental setup, compare three alternative methods, discuss the value of the measures of quality for KDSS proposed by [BGH97],and highlight some important numerical issues.
1882847
User Feedback-Based Refinement for Web Services Retrieval using Multiple Instance Learning
A critical step in the process of reusing existing WSDL-specified components is the discovery of potentially relevant Web services. Traditional category based Web service retrieval usually can achieve good recall but worse precision because some semantically relevant Web services are not actually relevant as they cannot provide suitable interfaces. In this paper, we present an interactive Web services retrieval mechanism to refine the coarse retrieval results set in category based retrieval. In the refinement, the signature matching of Web services that concerning the structure of operation specifications is investigated from a multi-instances view. In detail, each Web service is represented as a bag in multiple instance learning, while each operation in this Web service is regarded as an instance. This representation lies in that a user regards a service as useful if at least one operation provided by this Web service is useful. Experimental results show that our approach can improve the retrieval performance significantly: It can gain 83% precision in average after two rounds of user relevance feedback
12937715
Improvement of Color Video Demosaicking in Temporal Domain
Color demosaicking is critical to the image quality of digital still and video cameras that use a single-sensor array. Limited by the mosaic sampling pattern of the color filter array (CFA), color artifacts may occur in a demosaicked image in areas of high-frequency and/or sharp color transition structures. However, a color digital video camera captures a sequence of mosaic images and the temporal dimension of the color signals provides a rich source of information about the scene via camera and object motions. This paper proposes an inter-frame demosaicking approach to take advantage of all three forms of pixel correlations: spatial, spectral, and temporal. By motion estimation and statistical data fusion between adjacent mosaic frames, the new approach can remove much of the color artifacts that survive intra-frame demosaicking and also improve tone reproduction accuracy. Empirical results show that the proposed inter-frame demosaicking approach consistently outperforms its intra-frame counterparts both in peak signal-to-noise measure and subjective visual quality
11528752
Redundancy in Complete Sets
We show that a set is m-autoreducible if and only if it is m-mitotic. This solves a long standing open question in a surprising way. As a consequence of this unconditional result and recent work by Glaser et al. [12], complete sets for all of the following complexity classes are m-mitotic: NP, coNP, $\bigoplus$P, PSPACE, and NEXP, as well as all levels of PH, MODPH, and the Boolean hierarchy over NP. In the cases of NP, PSPACE, NEXP, and PH, this at once answers several well-studied open questions. These results tell us that complete sets share a redundancy that was not known before. In particular, every NP-complete set A splits into two NP-complete sets A1 and A2.
8272677
Parameter estimation for MRF stereo
This paper presents a novel approach for estimating parameters for MRF-based stereo algorithms. This approach is based on a new formulation of stereo as a maximum a posterior (MAP) problem, in which both a disparity map and MRF parameters are estimated from the stereo pair itself. We present an iterative algorithm for the MAP estimation that alternates between estimating the parameters while fixing the disparity map and estimating the disparity map while fixing the parameters. The estimated parameters include robust truncation thresholds, for both data and neighborhood terms, as well as a regularization weight. The regularization weight can be either a constant for the whole image, or spatially-varying, depending on local intensity gradients. In the latter case, the weights for intensity gradients are also estimated. Experiments indicate that our approach, as a wrapper for existing stereo algorithms, moves a baseline belief propagation stereo algorithm up six slots in the middlebury rankings.