52862068
Robust face alignment based on local texture classifiers
We propose a robust face alignment algorithm with a novel discriminative local texture model. Different from the conventional descriptive PCA local texture model in ASM, classifiers using LUT-type Haar-like features are trained from a large data set as local texture model. The strong discriminative power of the classifier greatly improves the accuracy and robustness of local searching on faces with expression variation and ambiguous contours. A Bayesian framework is configured for shape parameter optimization and the algorithm is implemented in a hierarchical structure for efficiency. Extensive experiments are reported to show its accuracy and robustness.
18786298
Robust Face Alignment Based on Hierarchical Classifier Network
Robust face alignment is crucial for many face processing applications. As face detection only gives a rough estimation of face region, one important problem is how to align facial shapes starting from this rough estimation, especially on face images with expression and pose changes. We propose a novel method of face alignment by building a hierarchical classifier network, connecting face detection and face alignment into a smooth coarse-to-fine procedure. Classifiers are trained to recognize feature textures in different scales from entire face to local patterns. A multi-layer structure is employed to organize the classifiers, which begins with one classifier at the first layer and gradually refines the localization of feature points by more classifiers in the following layers. A Bayesian framework is configured for the inference of the feature points between the layers. The boosted classifiers detects facial features discriminately from its local neighborhood, while the inference between the layers constrains the searching space. Extensive experiments are reported to show its accuracy and robustness.
1573162
Automated annotation of human faces in family albums
Automatic annotation of photographs is one of the most desirable needs in family photograph management systems. In this paper, we present a learning framework to automate the face annotation in family photograph albums. Firstly, methodologies of content-based image retrieval and face recognition are seamlessly integrated to achieve automated annotation. Secondly, face annotation is formulated in a Bayesian framework, in which the face similarity measure is defined as maximum a posteriori (MAP) estimation. Thirdly, to deal with the missing features, marginal probability is used so that samples which have missing features are compared with those having the full feature set to ensure a non-biased decision. The experimental evaluation has been conducted within a family album of few thousands of photographs and the results show that the proposed approach is effective and efficient in automated face annotation in family albums.
1111153
Towards the UML evaluation using taxonomic patterns on meta-classes
In order to evaluate the design quality of the UML, understanding meta-classes is a key activity as they are the primary weapons by which the UML specifies the application domains. The paper introduces taxonomic patterns for clustering the UML meta-classes based on the observation of their evolution and fitness. The result sets of the patterns assist in finding the evidence of the concerns about the UML design and quality. It not only helps to find out problematic meta-classes, possible design defects of the UML and the inconsistency between the UML meta-models and the application domains; but also provides valuable information for guiding the development and evaluation of the UML. The work can be the basis of further quality analysis of the UML meta-models.
15719994
IGroup: presenting web image search results in semantic clusters
Current web image search engines still rely on user typing textual description: query word(s) for visual targets. As the queries are often short, general or even ambiguous, the images in resulting pages vary in content and style. Thus, browsing with these results is likely to be tedious, frustrating and unpredictable.
 IGroup, a proposed image search engine addresses these problems by presenting the result in semantic clusters. The original result set was clustered in semantic groups with a cluster name relevant to user typed queries. Instead of looking through the result pages or modifying queries, IGroup users can refine findings to the interested sub-result sets with a navigational panel, where each cluster (sub-result set) was listed with a cluster name and representative thumbnails of the cluster.
 We compared IGroup with a general web image search engine: MSN, in term of efficiency, coverage, and satisfaction with a substantial user study. Our tool shows significant improvement in such criteria.
9760560
An edge-guided image interpolation algorithm via directional filtering and data fusion
Preserving edge structures is a challenge to image interpolation algorithms that reconstruct a high-resolution image from a low-resolution counterpart. We propose a new edge-guided nonlinear interpolation technique through directional filtering and data fusion. For a pixel to be interpolated, two observation sets are defined in two orthogonal directions, and each set produces an estimate of the pixel value. These directional estimates, modeled as different noisy measurements of the missing pixel are fused by the linear minimum mean square-error estimation (LMMSE) technique into a more robust estimate, using the statistics of the two observation sets. We also present a simplified version of the LMMSE-based interpolation algorithm to reduce computational cost without sacrificing much the interpolation performance. Experiments show that the new interpolation techniques can preserve edge sharpness and reduce ringing artifacts
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.