text: Given a query photo issued by a user (q-user), the landmark retrieval is to return a set of photos with their landmarks similar to those of the query, while the existing studies on the landmark retrieval focus on exploiting geometries of landmarks for similarity matches between candidate photos and a query photo. We observe that the same landmarks provided by different users over social media community may convey different geometry information depending on the viewpoints and or angles, and may subsequently yield very different results. In fact, dealing with the landmarks with caused by the photography of q-users is often nontrivial and has seldom been studied. In this paper we propose a novel framework, namely multi-query expansions, to retrieve semantically robust landmarks by two steps. Firstly, we identify the top- @math photos regarding the latent topics of a query landmark to construct multi-query set so as to remedy its possible . For this purpose, we significantly extend the techniques of Latent Dirichlet Allocation. Then, motivated by the typical methods, we propose to learn a deep networks based semantically, nonlinear and high-level features over the latent factor for landmark photo as the training set, which is formed by matrix factorization over user-photo matrix regarding the multi-query set. The learned deep network is further applied to generate the features for all the other photos, meanwhile resulting into a compact multi-query set within such space. Extensive experiments are conducted on real-world social media data with both landmark photos together with their user information to show the superior performance over the existing methods.
  @cite_37: Building intelligent systems that are capable of extracting high-level representations from high-dimensional sensory data lies at the core of solving many computer vision-related tasks. We propose the multispectral neural networks (MSNN) to learn features from multicolumn deep neural networks and embed the penultimate hierarchical discriminative manifolds into a compact representation. The low-dimensional embedding explores the complementary property of different views wherein the distribution of each view is sufficiently smooth and hence achieves robustness, given few labeled training data. Our experiments show that spectrally embedding several deep neural networks can explore the optimum output from the multicolumn networks and consistently decrease the error rate compared with a single deep network.
  @cite_41: Ranking on image search has attracted considerable attentions. Many graph-based algorithms have been proposed to solve this problem. Despite their remarkable success, these approaches are restricted to their separated image networks. To improve the ranking performance, one effective strategy is to work beyond the separated image graph by leveraging fruitful information from manual semantic labeling (i.e., tags) associated with images, which leads to the technique of co-ranking images and tags, a representative method that aims to explore the reinforcing relationship between image and tag graphs. The idea of co-ranking is implemented by adopting the paradigm of random walks. However, there are two problems hidden in co-ranking remained to be open: the high computational complexity and the problem of out-of-sample. To address the challenges above, in this paper, we cast the co-ranking process into a Bregman divergence optimization framework under which we transform the original random walk into an equivalent optimal kernel matrix learning problem. Enhanced by this new formulation, we derive a novel extension to achieve a better performance for both in-sample and out-of-sample cases. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of our approach.
  @cite_29: Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem. Among the existing methods, Low-Rank Representation (LRR) based method is quite superior in terms of its effectiveness, intuitiveness and robustness to noise corruptions. However, it aggressively tries to learn a common low-dimensional subspace for multi-view data, while inattentively ignoring the local manifold structure in each view, which is critically important to the spectral clustering; worse still, the low-rank minimization is enforced to achieve the data correlation consensus among all views, failing to flexibly preserve the local manifold structure for each view. In this paper, 1) we propose a multi-graph laplacian regularized LRR with each graph laplacian corresponding to one view to characterize its local manifold structure. 2) Instead of directly enforcing the low-rank minimization among all views for correlation consensus, we separately impose low-rank constraint on each view, coupled with a mutual structural consensus constraint, where it is able to not only well preserve the local manifold structure but also serve as a constraint for that from other views, which iteratively makes the views more agreeable. Extensive experiments on real-world multi-view data sets demonstrate its superiority.
  @cite_0: 
  @cite_43: 
  @cite_40: Many real-world objects described by multiple attributes or features can be decomposed as multiple "views" (e.g., an image can be described by a color view or a shape view), which often provides complementary information to each other. Learning a metric (similarity measures) for multi-view data is primary due to its wide applications in practices. However, leveraging multi-view information to produce a good metric is a great challenge and existing techniques are concerned with pairwise similarities, leading to undesirable fusion metric and high computational complexity. In this paper, we propose a novel Metric Fusion technique via cross-view graph Random Walk, named MFRW, regarding a multi-view based similarity graphs (with each similarity graph constructed under each view). Instead of using pairwise similarities, we seek a high-order metric yielded by graph random walks over constructed similarity graphs. Observing that outlier views" may exist in the fusion process, we incorporate the coefficient matrices representing the correlation strength between any two views into MFRW, named WMFRW. The principle of WMFRW is implemented by exploring the common latent structure" between views. The empirical studies conducted on real-world databases demonstrate that our approach outperforms the state-of-the-art competitors in terms of effectiveness and efficiency.
  @cite_59: 
  @cite_49: 
  @cite_31: 
  @cite_13: 
  @cite_17: Learning hash functions codes for similarity search over multi-view data is attracting increasing attention, where similar hash codes are assigned to the data objects characterizing consistently neighborhood relationship across views. Traditional methods in this category inherently suffer three limitations: 1) they commonly adopt a two-stage scheme where similarity matrix is first constructed, followed by a subsequent hash function learning; 2) these methods are commonly developed on the assumption that data samples with multiple representations are noise-free,which is not practical in real-life applications; and 3) they often incur cumbersome training model caused by the neighborhood graph construction using all N points in the database (O(N)). In this paper, we motivate the problem of jointly and efficiently training the robust hash functions over data objects with multi-feature representations which may be noise corrupted. To achieve both the robustness and training efficiency, we propose an approach to effectively and efficiently learning low-rank kernelized11We use kernelized similarity rather than kernel, as it is not a squared symmetric matrix for data-landmark affinity matrix. hash functions shared across views. Specifically, we utilize landmark graphs to construct tractable similarity matrices in multi-views to automatically discover neighborhood structure in the data. To learn robust hash functions, a latent low-rank kernel function is used to construct hash functions in order to accommodate linearly inseparable data. In particular, a latent kernelized similarity matrix is recovered by rank minimization on multiple kernel-based similarity matrices. Extensive experiments on real-world multi-view datasets validate the efficacy of our method in the presence of error corruptions.We use kernelized similarity rather than kernel, as it is not a squared symmetric matrix for data-landmark affinity matrix. A robust hashing method for multi-view data with noise corruptions is presented.It is to jointly learn a low-rank kernelized similarity consensus and hash functions.Approximate landmark graph is employed to make training fast.Extensive experiments are conducted on benchmarks to show the efficacy of our model.
summary: It is well known that learning deep network features plays a crucial role for a wide range of applications. Shao al @cite_37 proposed a multi-spectral neural networks (MSNN) is by projecting multiple penultimate layer of the deep network, namely multi-columns deep network, into multi-spectral smooth embedding to achieve the complementary multi-view spectral relationship. To this end, an effective multi-view alignment strategy is proposed. As a by-product, the smooth low-dimensional manifold embedding enables it to be robust to possible noise corruptions in raw feature representations. A multi-objective genetic program with four-layer structure is proposed in @cite_0 to automatically generate domain adaptive global feature descriptors for image classification. A typical yet simple deep network @cite_59 is learned for image classification, by employing PCA to learn multi-stage filter banks, followed by simple binary hashing and block histograms for indexing and pooling. Some multi-view feature fusion methods @cite_13 @cite_40 @cite_49 @cite_31 @cite_29 @cite_41 @cite_17 @cite_43 have also been proposed.