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Face photo-sketch and sketch-photo synthesis have important usages in law enforcement. It is challenging to synthesize face sketches from photos because the drawing techniques and styles of artists' depictions are hard to be learned. To synthesize face photos from sketches is also hard due to its ill-posed nature. In order to avoid mosaic effects in the existed photo-sketch methods, we propose a smoothness-constrained...
Visual saliency plays an important role in the human visual system HVS since it is indispensable for object detection and recognition. A bottom-up saliency model was proposed, following the manifold characteristic of HVS, previously developed for understanding HVS mechanism. The saliency of a given location of visual field is defined as the power of features responses after the dimensionality reduction...
In this paper, we propose a compact image signature based on VLAT. Our method integrates spatial information while significantly reducing the size of original VLAT by using two pojection steps. we carry out experiments showing our approach is competitive with state of the art signatures.
Sparsity-based super-resolution has attracted lots of attention. Due to the high dimensionality of image data, sparsity-based methods are often in a patch-wise manner and simply impose the smoothness constraints on the overlapped regions between reconstructed patches. However, the imposed smoothness constraint is commonly weak to regularize super-resolution problem when the observed low-resolution...
This paper presents a learning-based method called image super-resolution (SR) for generating a high-resolution (HR) image from a single low-resolution (LR) image. Recent research investigated the image SR problem using sparse coding, which is based on good reconstruction of any image local patch by a sparse linear combination of atoms from an overcomplete dictionary. However, sparse-coding-based...
In this paper, we present a theoretical analysis on learning anchors for local coordinate coding (LCC), which is a method to model functions for data lying on non-linear manifolds. In our analysis several local coding schemes, i.e., orthogonal coordinate coding (OC-C), local Gaussian coding (LGC), local Student coding (LSC), are theoretically compared, in terms of the upper-bound locality error on...
While the skeleton of a 2D shape corresponds to a planar graph, its encoding by usual graph data structures does not allow to capture its planar properties. Graph kernels may be defined on graph's encoding of the skeleton in order to define a similarity measure between shapes. Such graph kernels are usually based on a decomposition of graphs into bags of walks or trails. These linear patterns do not...
Insect species recognition is more difficult than generic object recognition because of the similarity between different species. In this paper, we propose a hybrid approach called discriminative local soft coding (DLSoft) which combines local and discriminative coding strategies together. Our method takes use of neighbor codewords to get a local soft coding and class specific codebooks (sets of codewords)...
With the recent explosion in the development of multimedia hardware capable of 3D display, 3D Picture Coding Sytems have assumed a pivotal role. While encoding techniques for stereo-scopic images is a well researched topic and compression standards such as MPEG provide variants to support it, compression of RGB-D data such as from the Microsoft Kinect sensor offers a number of unsolved challenges...
In this paper, we propose an unsupervised cluster method via a multi-task learning strategy, called Mt-Cluster. Our MtCluster learns a cluster-specific dictionary for each cluster to represent its sample signals and a shared common pattern pool (the commonality) for the essentially complemental representation. By treating learning the cluster-specific dictionary as a single task, MtCluster works in...
Spatial pyramid matching (SPM) component is part of most state-of-art image classification methods. SPM encodes spatial distribution of image features, in an un-supervised fashion, by partitioning an image into regions at multiple scales and concatenating feature vectors for these regions. In this paper we propose to replace the unsupervised SPM procedure with a supervised two-stage feature selection...
In text categorization, the dimensionality reduction methods, such as latent semantic indexing and nonnegative matrix factorization, commonly yield the dense representation that is not consistent with our common knowledge. On the other hand, the popular sparse coding methods are time-consuming and their dictionaries might contain negative entries, which is difficulty to interpret the semantic meaning...
Discriminative learning of sparse-code based dictionaries tends to be inherently unstable. We show that using a discriminative version of the deviation function to learn such dictionaries leads to a more stable formulation that can handle the reconstruction/discrimination trade-off in a principled manner. Results on Graz02 and UCF Sports datasets validate the proposed formulation.
Sparse coding seeks for over-complete bases to obtain the high-level image representation for image analysis. In many applications, the image data might reside on a low dimensional manifold embedded in high dimensional ambient space. However, standard sparse coding cannot exploit the manifold structure. In this paper, we propose a novel structured sparse coding method based on the L1-graph, in which...
In this paper, we propose a fully automatic approach for person-independent 3D facial expression recognition. In order to extract discriminative expression features, each aligned 3D facial surface is compactly represented as multiple global histograms of local normal patterns from multiple normal components and multiple binary encoding scales, namely Multi-Scale Local Normal Patterns (MS-LNPs). 3D...
Along with the ever-growing Web, horror video sharing through the Internet has affected our children's psychological health. Most of current horror video filtering researches pay more attention to the extraction of global features or selection of an optimal classifier, while neglecting the underlying contexts in a scene. In this paper, a novel cost-sensitive sparse coding (CSC) model is proposed to...
Recent years have seen an increasing interest in codebook-based model(bag-of-words-BOW) for image representation, which includes the basic bag-of-words model and its improved version for local descriptor reconstruction with sparse coding (SC) and locality-constrained linear coding (LLC) etc. Although the recent coding strategies in the BoW model can lead to prospect performance using large amounts...
In this paper we present a new method for object categorization. Firstly an image representation is obtained by the proposed hierarchical learning method consisting of alternating between local coding and maximum pooling operations, where the local coding operation induces discrimination while the image descriptor and maximum pooling operation induces invariance in hierarchical architecture. Then...
The locality and sparsity constrained encoding methods have shown the good image classification performance in recent papers. Among these methods, the common strategy is encoding one descriptor into one code by a learned codebook and then applying SPM and Pooling strategy to get the final image representation. However, the ignorance of local spatial context has been a barrier to improve their discriminative...
In this paper, a novel sparse representation based super-resolution (SR) method is proposed to reconstruct a high resolution (HR) face image from a low resolution (LR) observation via training samples. First, a specific LR and HR over-complete dictionary pair is learned for a certain patch over the patches in all training samples with the same position. Second, K-selection mean constrain is used to...
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