Ying Yuan

Bio: Ying Yuan is an academic researcher from Zhejiang University. The author has contributed to research in topics: Automatic image annotation & Discriminative model. The author has an hindex of 4, co-authored 6 publications receiving 58 citations.

Heterogeneous feature selection by group lasso with logistic regression

Abstract: The selection of groups of discriminative features is critical for image understanding since the irrelevant features could deteriorate the performance of image understanding. This paper formulates the selection of groups of discriminative features by the extension of group lasso with logistic regression for high-dimensional feature setting, we call it as the heterogeneous feature selection by Group Lasso with Logistic Regression (GLLR). GLLR encodes a sparse grouping prior to seek after a more interpretable model for feature selection and can identify most of discriminative groups of homogeneous features. The utilization of GLLR for image annotation shows the proposed GLLR achieves a better performance.

Image annotation by semi-supervised cross-domain learning with group sparsity

Abstract: With the explosive growth of multimedia data in the web, multi-label image annotation has been attracted more and more attention. Although the amount of available data is large and growing, the number of labeled data is quite small. This paper proposes an approach to utilize both unlabeled data in target domain and labeled data in auxiliary domain to boost the performance of image annotation. Moreover, since different kinds of heterogeneous features in images have different intrinsic discriminative power for image understanding, group sparsity is introduced in our approach to effectively utilize those heterogeneous visual features with data of target and auxiliary domains. We call this approach semi-supervised cross-domain learning with group sparsity (S^2CLGS). The strength of the proposed S^2CLGS method for multi-label image annotation is to integrate semi-supervised discriminant analysis, cross-domain learning and sparse coding together. Experiments demonstrate the effectiveness of S^2CLGS in comparison with other image annotation algorithms.

Image annotation by composite kernel learning with group structure

Abstract: We can obtain more and more kinds of heterogeneous features (such as color, shape and texture) in images which can be extracted to describe various aspects of visual characteristics. Those high-dimensional heterogeneous visual features are intrinsically embedded in a non-linear space. In order to effectively utilize these heterogeneous features, this paper proposes an approach, called Composite Kernel Learning with Group Structure (CKLGS), to select groups of discriminative features for image annotation. For each image label, the CKLGS method embeds the nonlinear image data with discriminative features into different Reproducing Kernel Hilbert Spaces (RKHS), and then composes these kernels to select groups of discriminative features. Thus a classification model can be trained for image annotation. By the comparisons with other image annotation algorithms, experiments show that the proposed CKLGS for image annotation achieves a better performance.

Annotating web images using NOVA: NOn-conVex group spArsity

Abstract: As image feature vector is large, selecting the right features plays a fundamental role in Web image annotation. Most existing approaches are either based on individual feature selection, which leads to local optima, or using a convex penalty, which leads to inconsistency. To address these difficulties, in this paper we propose a new sparsity-based approach NOVA (NOn-conVex group spArsity). To the best of our knowledge, NOVA is the first to introduce non-convex penalty for group selection in high-dimensional heterogeneous features space. Because it is a group-sparsity approach, it approximately reaches global optima. Because it uses non-convex penalty, it achieves the consistency. We demonstrate the superior performance of NOVA via three means. First, we present theoretical proof that NOVA is consistent, satisfying un-biasness, sparsity and continuity. Second, we show NOVA converges to the true underlying model by using a ground-truth-available generative-model simulation. Third, we report extensive experimental results on three diverse and widely-used data sets Kodak, MSRA-MM 2.0, and NUS-WIDE. We also compare NOVA against the state-of-the-art approaches, and report superior experimental results.

Joint Feature Selection and Subspace Learning for Cross-Modal Retrieval

Abstract: Cross-modal retrieval has recently drawn much attention due to the widespread existence of multimodal data. It takes one type of data as the query to retrieve relevant data objects of another type, and generally involves two basic problems: the measure of relevance and coupled feature selection. Most previous methods just focus on solving the first problem. In this paper, we aim to deal with both problems in a novel joint learning framework. To address the first problem, we learn projection matrices to map multimodal data into a common subspace, in which the similarity between different modalities of data can be measured. In the learning procedure, the $\ell _{21}$ -norm penalties are imposed on the projection matrices separately to solve the second problem, which selects relevant and discriminative features from different feature spaces simultaneously. A multimodal graph regularization term is further imposed on the projected data,which preserves the inter-modality and intra-modality similarity relationships.An iterative algorithm is presented to solve the proposed joint learning problem, along with its convergence analysis. Experimental results on cross-modal retrieval tasks demonstrate that the proposed method outperforms the state-of-the-art subspace approaches.

Assistive tagging: A survey of multimedia tagging with human-computer joint exploration

Abstract: Along with the explosive growth of multimedia data, automatic multimedia tagging has attracted great interest of various research communities, such as computer vision, multimedia, and information retrieval. However, despite the great progress achieved in the past two decades, automatic tagging technologies still can hardly achieve satisfactory performance on real-world multimedia data that vary widely in genre, quality, and content. Meanwhile, the power of human intelligence has been fully demonstrated in the Web 2.0 era. If well motivated, Internet users are able to tag a large amount of multimedia data. Therefore, a set of new techniques has been developed by combining humans and computers for more accurate and efficient multimedia tagging, such as batch tagging, active tagging, tag recommendation, and tag refinement. These techniques are able to accomplish multimedia tagging by jointly exploring humans and computers in different ways. This article refers to them collectively as assistive tagging and conducts a comprehensive survey of existing research efforts on this theme. We first introduce the status of automatic tagging and manual tagging and then state why assistive tagging can be a good solution. We categorize existing assistive tagging techniques into three paradigms: (1) tagging with data selection & organization; (2) tag recommendation; and (3) tag processing. We introduce the research efforts on each paradigm and summarize the methodologies. We also provide a discussion on several future trends in this research direction.

A convex formulation for semi-supervised multi-label feature selection

Abstract: Explosive growth of multimedia data has brought challenge of how to efficiently browse, retrieve and organize these data. Under this circumstance, different approaches have been proposed to facilitate multimedia analysis. Several semi-supervised feature selection algorithms have been proposed to exploit both labeled and unlabeled data. However, they are implemented based on graphs, such that they cannot handle large-scale datasets. How to conduct semi-supervised feature selection on large-scale datasets has become a challenging research problem. Moreover, existing multi-label feature selection algorithms rely on eigendecomposition with heavy computational burden, which further prevent current feature selection algorithms from being applied for big data. In this paper, we propose a novel convex semi-supervised multi-label feature selection algorithm, which can be applied to large-scale datasets. We evaluate performance of the proposed algorithm over five benchmark datasets and compare the results with state-of-the-art supervised and semi-supervised feature selection algorithms as well as baseline using all features. The experimental results demonstrate that our proposed algorithm consistently achieve superiors performances.

Web Image Annotation Via Subspace-Sparsity Collaborated Feature Selection

Abstract: The number of web images has been explosively growing due to the development of network and storage technology. These images make up a large amount of current multimedia data and are closely related to our daily life. To efficiently browse, retrieve and organize the web images, numerous approaches have been proposed. Since the semantic concepts of the images can be indicated by label information, automatic image annotation becomes one effective technique for image management tasks. Most existing annotation methods use image features that are often noisy and redundant. Hence, feature selection can be exploited for a more precise and compact representation of the images, thus improving the annotation performance. In this paper, we propose a novel feature selection method and apply it to automatic image annotation. There are two appealing properties of our method. First, it can jointly select the most relevant features from all the data points by using a sparsity-based model. Second, it can uncover the shared subspace of original features, which is beneficial for multi-label learning. To solve the objective function of our method, we propose an efficient iterative algorithm. Extensive experiments are performed on large image databases that are collected from the web. The experimental results together with the theoretical analysis have validated the effectiveness of our method for feature selection, thus demonstrating its feasibility of being applied to web image annotation.

Graph self-representation method for unsupervised feature selection

Abstract: Both subspace learning methods and feature selection methods are often used for removing irrelative features from high-dimensional data. Studies have shown that feature selection methods have interpretation ability and subspace learning methods output stable performance. This paper proposes a new unsupervised feature selection by integrating a subspace learning method (i.e., Locality Preserving Projection (LPP)) into a new feature selection method (i.e., a sparse feature-level self-representation method), aim at simultaneously receiving stable performance and interpretation ability. Different from traditional sample-level self-representation where each sample is represented by all samples and has been popularly used in machine learning and computer vision. In this paper, we propose to represent each feature by its relevant features to conduct feature selection via devising a feature-level self-representation loss function plus an l 2 , 1 -norm regularization term. Then we add a graph regularization term (i.e., LPP) into the resulting feature selection model to simultaneously conduct feature selection and subspace learning. The rationale of the LPP regularization term is that LPP preserves the original distribution of data after removing irrelative features. Finally, we conducted experiments on UCI data sets and other real data sets and the experimental results showed that the proposed approach outperformed all comparison algorithms.