The Experts below are selected from a list of 173034 Experts worldwide ranked by ideXlab platform
Qi Tian - One of the best experts on this subject based on the ideXlab platform.
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Bundled Local Features for Image Representation
IEEE Transactions on Circuits and Systems for Video Technology, 2018Co-Authors: Chunjie Zhang, Jitao Sang, Guibo Zhu, Qi TianAbstract:Local features have been widely used for Image Representation. Traditional methods often treat each local feature independently or simply model the correlations of local features with spatial partition. However, local features are correlated and should be jointly modeled. Besides, due to the variety of Images, predefined partition rules will probably introduce noisy information. To solve these problems, in this paper we propose a novel bundled local features method for efficient Image Representation and apply it for classification. Specially, we first extract local features and bundle them together with over-complete spatial shapes by viewing each local feature as the central point. Then, the most discriminatively bundling features are selected by reconstruction error minimization. The encoding parameters are then used for Image Representations in a matrix form. Finally, we train bi-linear classifiers with quadratic hinge loss to predict the classes of Images. The proposed method can combine local features appropriately and efficiently for discriminative Representations. Experimental results on three Image data sets show the effectiveness of the proposed method compared with other local features combination strategies.
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Joint Image Representation and classification in random semantic spaces
Neurocomputing, 2015Co-Authors: Chunjie Zhang, Liang Li, Yifan Zhang, Qingming Huang, Qi TianAbstract:Local feature based Image Representation has been widely used for Image classification in recent years. Although this strategy has been proven very effective, the Image Representation and classification processes are relatively independent. This means the Image classification performance may be hindered by the Representation efficiency. To jointly consider the Image Representation and classification in an unified framework, in this paper, we propose a novel algorithm by combining Image Representation and classification in the random semantic spaces. First, we encode local features with the sparse coding technique and use the encoding parameters for raw Image Representation. These Image Representations are then randomly selected to generate the random semantic spaces and Images are then mapped to these random semantic spaces by classifier training. The mapped semantic Representation is then used as the final Image Representation. In this way, we are able to jointly consider the Image Representation and classification in order to achieve better performances. We evaluate the performances of the proposed method on several public Image datasets and experimental results prove the proposed method?s effectiveness. HighlightsWe jointly consider Image Representation and classification in unified framework.Images are randomly selected for semantic space construction by training classifiers.We use random semantic spaces for Image Representation and class prediction.We achieve the state-of-the-art performance on several public datasets.
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Robust Image Representation for classification and retrieval
2013Co-Authors: Qi TianAbstract:How to represent visual information is crucial for computers to analyze and understand massive Images. The objective of my research is to construct a robust Image Representation for efficient and effective visual object recognition and retrieval. Bag-of-Words model (BoW) has shown its superiority over many conventional global features in Image classification and retrieval systems [1][5][25]. However, the large quantization error may degrade the effectiveness of the BoW Representation [2]. To address this problem, several soft quantization based methods have been proposed in literature [3][4]. Nevertheless, these methods are not efficient enough when applied on large dataset, and their effectiveness is still unsatisfied. We propose a new model of Image Representation based on a multi-layer codebook. In this method, we first construct a multi-layer codebook by explicitly reducing the quantization error in a global or local manner. Then we use parallel or hierarchically connected visual codebooks to quantize each local feature into multiple visual words. It yields a more precise Representation to describe the distribution of local features in the visual space. The above Representation disregards the spatial configuration of visual features. Spatial Pyramid Matching (SPM) [5] has been proposed to extend the BoW model for object classification. By encoding global Image positions of local features, it makes Image matching more accurate. However, for unaligned Images, where the object is rotated, flipped or translated, SPM loses its discriminative power. We propose some new spatial pooling strategies to deal with various transformation variations. The spatial configurations of visual features in both local area and global area are taken into our consideration to generate a more robust Image Representation. Furthermore, given one Image Representation, it is hard to retrieve all the similar Images from a large-scale Image database. To enrich the given Image example, query expansion method has been used in Image retrieval systems [4][93][94]. These methods take either whole Images or matching regions as new queries, which can unavoidably add irrelevant features into the query. To minimize the irrelevant visual features introduced by query expansion, we proposed a new query expansion approach. The Image Representation produced by our method can retrieve more Images that are relevant, and it remains consistent with the original Representation in the meanwhile. We conduct extensive experiments on some well-known benchmark datasets for Image classification and retrieval task. Experimental results demonstrate that our methods can further improve the performance compared with the state-of-the-arts. Besides, the proposed Image Representation is compact and consistent with the BoW model, which makes it applicable to Image retrieval and other related task in computer vision as well.
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spatial pooling for transformation invariant Image Representation
ACM Multimedia, 2011Co-Authors: Xia Li, Yan Song, Yijuan Lu, Qi TianAbstract:Spatial Pyramid Matching (SPM) [2] has been proposed to extend the Bag-of-Word (BoW) model for object classification. By re-serving the finer level information, it makes Image matching more accurate. However, for not well-aligned Images, where the object is rotated, flipped or translated, SPM may lose its discrimination power. To tackle this problem, we propose novel spatial pooling layouts to address various transformations, and generate a more general Image Representation. To evaluate the effectiveness of the proposed approach, we conduct extensive experiments on three transformation emphasized datasets for object classification task. Experimental results demonstrate its superiority over the state-of-the-arts. Besides, the proposed Image Representation is compact and consistent with the BoW model, which makes it applicable to Image retrieval task as well.
C. V. Jawahar - One of the best experts on this subject based on the ideXlab platform.
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Bringing semantics into word Image Representation
Pattern Recognition, 2020Co-Authors: Praveen Krishnan, C. V. JawaharAbstract:Abstract The shift from one-hot to distributed Representation, popularly referred to as word embedding has changed the landscape of natural language processing ( nlp ) and information retrieval ( ir ) communities. In the domain of document Images, we have always appreciated the need for learning a holistic word Image Representation which is popularly used for the task of word spotting. The Representations proposed for word spotting is different from word embedding in text since the later captures the semantic aspects of the word which is a crucial ingredient to numerous nlp and ir tasks. In this work, we attempt to encode the notion of semantics into word Image Representation by bringing the advancements from the textual domain. We propose two novel forms of Representations where the first form is designed to be inflection invariant by focusing on the approximate linguistic root of the word, while the second form is built along the lines of recent textual word embedding techniques such as Word2Vec. We observe that such Representations are useful for both traditional word spotting and also enrich the search results by accounting the semantic nature of the task. We conduct our experiments on the challenging document Images taken from historical-modern collections, handwritten-printed domains, and Latin-Indic scripts. For the purpose of semantic evaluation, we have prepared a large synthetic word Image dataset and report interesting results for the standard semantic evaluation metrics such as word analogy and word similarity.
Deng Cai - One of the best experts on this subject based on the ideXlab platform.
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Orthogonal Projective Sparse Coding for Image Representation
Neurocomputing, 2016Co-Authors: Wei Zhao, Ziyu Guan, Zheng Liu, Binbin Lin, Deng CaiAbstract:We consider the problem of Image Representation for visual analysis. When representing Images as vectors, the feature space is of very high dimensionality, which makes it difficult for applying statistical techniques for visual analysis. One then hope to apply matrix factorization techniques, such as Singular Vector Decomposition (SVD) to learn the low dimensional hidden concept space. Among various matrix factorization techniques, sparse coding receives considerable interests in recent years because its sparse Representation leads to an elegant interpretation. However, most of the existing sparse coding algorithms are computational expensive since they compute the basis vectors and the Representations iteratively. In this paper, we propose a novel method, called Orthogonal Projective Sparse Coding (OPSC), for efficient and effective Image Representation and analysis. Integrating the techniques from manifold learning and sparse coding, OPSC provides a sparse Representation which can capture the intrinsic geometric structure of the Image space. Extensive experimental results on real world applications demonstrate the effectiveness and efficiency of the proposed approach.
Ziyu Guan - One of the best experts on this subject based on the ideXlab platform.
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Orthogonal Projective Sparse Coding for Image Representation
Neurocomputing, 2016Co-Authors: Wei Zhao, Ziyu Guan, Zheng Liu, Binbin Lin, Deng CaiAbstract:We consider the problem of Image Representation for visual analysis. When representing Images as vectors, the feature space is of very high dimensionality, which makes it difficult for applying statistical techniques for visual analysis. One then hope to apply matrix factorization techniques, such as Singular Vector Decomposition (SVD) to learn the low dimensional hidden concept space. Among various matrix factorization techniques, sparse coding receives considerable interests in recent years because its sparse Representation leads to an elegant interpretation. However, most of the existing sparse coding algorithms are computational expensive since they compute the basis vectors and the Representations iteratively. In this paper, we propose a novel method, called Orthogonal Projective Sparse Coding (OPSC), for efficient and effective Image Representation and analysis. Integrating the techniques from manifold learning and sparse coding, OPSC provides a sparse Representation which can capture the intrinsic geometric structure of the Image space. Extensive experimental results on real world applications demonstrate the effectiveness and efficiency of the proposed approach.
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Image Representation using Laplacian regularized nonnegative tensor factorization
Pattern Recognition, 2011Co-Authors: Can Wang, Zhengguang Chen, Chun Chen, Ziyu GuanAbstract:Tensor provides a better Representation for Image space by avoiding information loss in vectorization. Nonnegative tensor factorization (NTF), whose objective is to express an n-way tensor as a sum of k rank-1 tensors under nonnegative constraints, has recently attracted a lot of attentions for its efficient and meaningful Representation. However, NTF only sees Euclidean structures in data space and is not optimized for Image Representation as Image space is believed to be a sub-manifold embedded in high-dimensional ambient space. To avoid the limitation of NTF, we propose a novel Laplacian regularized nonnegative tensor factorization (LRNTF) method for Image Representation and clustering in this paper. In LRNTF, the Image space is represented as a 3-way tensor and we explicitly consider the manifold structure of the Image space in factorization. That is, two data points that are close to each other in the intrinsic geometry of Image space shall also be close to each other under the factorized basis. To evaluate the performance of LRNTF in Image Representation and clustering, we compare our algorithm with NMF, NTF, NCut and GNMF methods on three standard Image databases. Experimental results demonstrate that LRNTF achieves better Image clustering performance, while being more insensitive to noise.
Caideng - One of the best experts on this subject based on the ideXlab platform.
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Orthogonal Projective Sparse Coding for Image Representation
Neurocomputing, 2016Co-Authors: Zhaowei, Liuzheng, Guanziyu, Linbinbin, CaidengAbstract:We consider the problem of Image Representation for visual analysis. When representing Images as vectors, the feature space is of very high dimensionality, which makes it difficult for applying sta...
