A sparse embedding and least variance encoding approach to hashing

Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection

In this paper, we propose a new unsupervised spectral feature selection model by embedding a graph regularizer into the framework of joint sparse regression for preserving the local structures of data. To do this, we first extract the bases of training data by previous dictionary learning methods and, then, map original data into the basis space to generate their new representations, by proposing a novel joint graph sparse coding (JGSC) model. In JGSC, we first formulate its objective function by simultaneously taking subspace learning and joint sparse regression into account, then, design a new optimization solution to solve the resulting objective function, and further prove the convergence of the proposed solution. Furthermore, we extend JGSC to a robust JGSC (RJGSC) via replacing the least square loss function with a robust loss function, for achieving the same goals and also avoiding the impact of outliers. Finally, experimental results on real data sets showed that both JGSC and RJGSC outperformed the state-of-the-art algorithms in terms of k -nearest neighbor classification performance.

A Deep Convolutional Neural Network-Based Framework for Automatic Fetal Facial Standard Plane Recognition

Ultrasound imaging has become a prevalent examination method in prenatal diagnosis. Accurate acquisition of fetal facial standard plane (FFSP) is the most important precondition for subsequent diagnosis and measurement. In the past few years, considerable effort has been devoted to FFSP recognition using various hand-crafted features, but the recognition performance is still unsatisfactory due to the high intraclass variation of FFSPs and the high degree of visual similarity between FFSPs and other non-FFSPs. To improve the recognition performance, we propose a method to automatically recognize FFSP via a deep convolutional neural network (DCNN) architecture. The proposed DCNN consists of 16 convolutional layers with small 3 × 3 size kernels and three fully connected layers. A global average pooling is adopted in the last pooling layer to significantly reduce network parameters, which alleviates the overfitting problems and improves the performance under limited training data. Both the transfer learning strategy and a data augmentation technique tailored for FFSP are implemented to further boost the recognition performance. Extensive experiments demonstrate the advantage of our proposed method over traditional approaches and the effectiveness of DCNN to recognize FFSP for clinical diagnosis.

Learning to Hash With Optimized Anchor Embedding for Scalable Retrieval

Sparse representation and image hashing are powerful tools for data representation and image retrieval respectively. The combinations of these two tools for scalable image retrieval, i.e., sparse hashing (SH) methods, have been proposed in recent years and the preliminary results are promising. The core of those methods is a scheme that can efficiently embed the (high-dimensional) image features into a low-dimensional Hamming space, while preserving the similarity between features. Existing SH methods mostly focus on finding better sparse representations of images in the hash space. We argue that the anchor set utilized in sparse representation is also crucial, which was unfortunately underestimated by the prior art. To this end, we propose a novel SH method that optimizes the integration of the anchors, such that the features can be better embedded and binarized, termed as Sparse Hashing with Optimized Anchor Embedding. The central idea is to push the anchors far from the axis while preserving their relative positions so as to generate similar hashcodes for neighboring features. We formulate this idea as an orthogonality constrained maximization problem and an efficient and novel optimization framework is systematically exploited. Extensive experiments on five benchmark image data sets demonstrate that our method outperforms several state-of-the-art related methods.

Latent Semantic Minimal Hashing for Image Retrieval

Hashing-based similarity search is an important technique for large-scale query-by-example image retrieval system, since it provides fast search with computation and memory efficiency. However, it is a challenge work to design compact codes to represent original features with good performance. Recently, a lot of unsupervised hashing methods have been proposed to focus on preserving geometric structure similarity of the data in the original feature space, but they have not yet fully refined image features and explored the latent semantic feature embedding in the data simultaneously. To address the problem, in this paper, a novel joint binary codes learning method is proposed to combine image feature to latent semantic feature with minimum encoding loss, which is referred as latent semantic minimal hashing. The latent semantic feature is learned based on matrix decomposition to refine original feature, thereby it makes the learned feature more discriminative. Moreover, a minimum encoding loss is combined with latent semantic feature learning process simultaneously, so as to guarantee the obtained binary codes are discriminative as well. Extensive experiments on several well-known large databases demonstrate that the proposed method outperforms most state-of-the-art hashing methods.

Fast Neuroimaging-Based Retrieval for Alzheimer's Disease Analysis

This paper proposes a framework of fast neuroimaging-based retrieval and AD analysis, by three key steps: (1) landmark detection, which efficiently extracts landmark-based neuroimaging features without the need of nonlinear registration in testing stage; (2) landmark selection, which removes redundant/noisy landmarks via proposing a feature selection method that considers structural information among landmarks; and (3) hashing, which converts high-dimensional features of subjects into binary codes, for efficiently conducting approximate nearest neighbor search and diagnosis of AD. We have conducted experiments on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and demonstrated that our framework could achieve higher performance than the comparison methods, in terms of accuracy and speed (at least 100 times faster).

Non-Local Auto-Encoder With Collaborative Stabilization for Image Restoration

Deep neural networks have been applied to image restoration to achieve the top-level performance. From a neuroscience perspective, the layerwise abstraction of knowledge in a deep neural network can, to some extent, reveal the mechanisms of how visual cues are processed in human brain. A pivotal property of human brain is that similar visual cues can stimulate the same neuron to induce similar neurological signals. However, conventional neural networks do not consider this property, and the resulting models are, as a result, unstable regarding their internal propagation. In this paper, we develop the (stacked) non-local auto-encoder, which exploits self-similar information in natural images for stability. We propose that similar inputs should induce similar network propagation. This is achieved by constraining the difference between the hidden representations of non-local similar image blocks during training. By applying the proposed model to image restoration, we then develop a collaborative stabilization step to further rectify forward propagation. To obtain a reliable deep model, we employ several strategies to simplify training and improve testing. Extensive image restoration experiments, including image denoising and super-resolution, demonstrate the effectiveness of the proposed method.

Compact Structure Hashing via Sparse and Similarity Preserving Embedding

Over the past few years, fast approximate nearest neighbor (ANN) search is desirable or essential, e.g., in huge databases, and therefore many hashing-based ANN techniques have been presented to return the nearest neighbors of a given query from huge databases. Hashing-based ANN techniques have become popular due to its low memory cost and good computational complexity. Recently, most of hashing methods have realized the importance of the relationship of the data and exploited the different structure of data to improve retrieval performance. However, a limitation of the aforementioned methods is that the sparse reconstructive relationship of the data is neglected. In this case, few methods can find the discriminating power and own the local properties of the data for learning compact and effective hash codes. To take this crucial issue into account, this paper proposes a method named special structure-based hashing (SSBH). SSBH can preserve the underlying geometric information among the data, and exploit the prior information that there exists sparse reconstructive relationship of the data, for learning compact and effective hash codes. Upon extensive experimental results, SSBH is demonstrated to be more robust and more effective than state-of-the-art hashing methods.

Subspace Regularized Sparse Multitask Learning for Multiclass Neurodegenerative Disease Identification

The high feature-dimension and low sample-size problem is one of the major challenges in the study of computer-aided Alzheimer's disease (AD) diagnosis. To circumvent this problem, feature selection and subspace learning have been playing core roles in the literature. Generally, feature selection methods are preferable in clinical applications due to their ease for interpretation, but subspace learning methods can usually achieve more promising results. In this paper, we combine two different methodological approaches to discriminative feature selection in a unified framework. Specifically, we utilize two subspace learning methods, namely, linear discriminant analysis and locality preserving projection, which have proven their effectiveness in a variety of fields, to select class-discriminative and noise-resistant features. Unlike previous methods in neuroimaging studies that mostly focused on a binary classification, the proposed feature selection method is further applicable for multiclass classification in AD diagnosis. Extensive experiments on the Alzheimer's disease neuroimaging initiative dataset showed the effectiveness of the proposed method over other state-of-the-art methods.

Block-Row Sparse Multiview Multilabel Learning for Image Classification

In image analysis, the images are often represented by multiple visual features (also known as multiview features), that aim to better interpret them for achieving remarkable performance of the learning. Since the processes of feature extraction on each view are separated, the multiple visual features of images may include overlap, noise, and redundancy. Thus, learning with all the derived views of the data could decrease the effectiveness. To address this, this paper simultaneously conducts a hierarchical feature selection and a multiview multilabel (MVML) learning for multiview image classification, via embedding a proposed a new block-row regularizer into the MVML framework. The block-row regularizer concatenating a Frobenius norm (F-norm) regularizer and an l(2,1)-norm regularizer is designed to conduct a hierarchical feature selection, in which the F-norm regularizer is used to conduct a high-level feature selection for selecting the informative views (i.e., discarding the uninformative views) and the l(2,1)-norm regularizer is then used to conduct a low-level feature selection on the informative views. The rationale of the use of a block-row regularizer is to avoid the issue of the over-fitting (via the block-row regularizer), to remove redundant views and to preserve the natural group structures of data (via the F-norm regularizer), and to remove noisy features (the l(2,1)-norm regularizer), respectively. We further devise a computationally efficient algorithm to optimize the derived objective function and also theoretically prove the convergence of the proposed optimization method. Finally, the results on real image datasets show that the proposed method outperforms two baseline algorithms and three state-of-the-art algorithms in terms of classification performance.

Bit-Scalable Deep Hashing With Regularized Similarity Learning for Image Retrieval and Person Re-Identification

Extracting informative image features and learning effective approximate hashing functions are two crucial steps in image retrieval. Conventional methods often study these two steps separately, e.g., learning hash functions from a predefined hand-crafted feature space. Meanwhile, the bit lengths of output hashing codes are preset in the most previous methods, neglecting the significance level of different bits and restricting their practical flexibility. To address these issues, we propose a supervised learning framework to generate compact and bit-scalable hashing codes directly from raw images. We pose hashing learning as a problem of regularized similarity learning. In particular, we organize the training images into a batch of triplet samples, each sample containing two images with the same label and one with a different label. With these triplet samples, we maximize the margin between the matched pairs and the mismatched pairs in the Hamming space. In addition, a regularization term is introduced to enforce the adjacency consistency, i.e., images of similar appearances should have similar codes. The deep convolutional neural network is utilized to train the model in an end-to-end fashion, where discriminative image features and hash functions are simultaneously optimized. Furthermore, each bit of our hashing codes is unequally weighted, so that we can manipulate the code lengths by truncating the insignificant bits. Our framework outperforms state-of-the-arts on public benchmarks of similar image search and also achieves promising results in the application of person re-identification in surveillance. It is also shown that the generated bit-scalable hashing codes well preserve the discriminative powers with shorter code lengths.

A novel relational regularization feature selection method for joint regression and classification in AD diagnosis

In this paper, we focus on joint regression and classification for Alzheimer's disease diagnosis and propose a new feature selection method by embedding the relational information inherent in the observations into a sparse multi-task learning framework. Specifically, the relational information includes three kinds of relationships (such as feature-feature relation, response-response relation, and sample-sample relation), for preserving three kinds of the similarity, such as for the features, the response variables, and the samples, respectively. To conduct feature selection, we first formulate the objective function by imposing these three relational characteristics along with an ℓ_2,1-norm regularization term, and further propose a computationally efficient algorithm to optimize the proposed objective function. With the dimension-reduced data, we train two support vector regression models to predict the clinical scores of ADAS-Cog and MMSE, respectively, and also a support vector classification model to determine the clinical label. We conducted extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to validate the effectiveness of the proposed method. Our experimental results showed the efficacy of the proposed method in enhancing the performances of both clinical scores prediction and disease status identification, compared to the state-of-the-art methods.

Multi-view Classification for Identification of Alzheimer's Disease

In this paper, we propose a multi-view learning method using Magnetic Resonance Imaging (MRI) data for Alzheimer's Disease (AD) diagnosis. Specifically, we extract both Region-Of-Interest (ROI) features and Histograms of Oriented Gradient (HOG) features from each MRI image, and then propose mapping HOG features onto the space of ROI features to make them comparable and to impose high intra-class similarity with low inter-class similarity. Finally, both mapped HOG features and original ROI features are input to the support vector machine for AD diagnosis. The purpose of mapping HOG features onto the space of ROI features is to provide complementary information so that features from different views can not only be comparable (i.e., homogeneous) but also be interpretable. For example, ROI features are robust to noise, but lack of reflecting small or subtle changes, while HOG features are diverse but less robust to noise. The proposed multi-view learning method is designed to learn the transformation between two spaces and to separate the classes under the supervision of class labels. The experimental results on the MRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that the proposed multi-view method helps enhance disease status identification performance, outperforming both baseline methods and state-of-the-art methods.

Full-space local topology extraction for cross-modal retrieval

With the ever increasing availability of various kinds of multimedia data, cross-modal retrieval, which enables information retrieval from various types of data given various types of query, has become a research hotspot. Hashing-based techniques have been developed to solve this problem, however, most previous works cannot capture the shared underlying structure of real-world multimodal data, which degrades their retrieval performances. In this paper, we propose a novel hashing method based on the extraction of the common manifold structure shared among different feature spaces. To faithfully represent the common structure, two kinds of local topology information are exploited in our method. Local angles are incorporated within the extraction of local topology of each feature space, which is then used to learn a common intermediate subspace. After heterogeneous features being embedded into this subspace, local similarities are exploited to extract the local topology between different feature spaces, and learn compact Hamming embeddings to facilitate cross-modal retrieval. The proposed method is referred to as full-space local topology extraction for hashing. Extensive comparisons with other state-of-the-art methods on three benchmark multimedia data sets demonstrate the superiority of our proposed method in terms of retrieval recall and search accuracy.