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Journal ArticleDOI

Higher-level feature combination via multiple kernel learning for image classification

Wei Luo1, Jian Yang1, Wei Xu1, Jun Li1, Jian Zhang1 
01 Nov 2015-Neurocomputing (Elsevier)-Vol. 167, pp 209-217
TL;DR: A soft salient coding method is proposed, which overcomes the information suppression problem in the original salient coding (SaC) method and is proposed using multiple kernel learning (MKL) to combine these features for classification tasks.
About: This article is published in Neurocomputing.The article was published on 2015-11-01. It has received 12 citations till now. The article focuses on the topics: Feature (computer vision) & Feature extraction.
Citations
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Journal ArticleDOI
Haiyong Zheng1, Ruchen Wang1, Zhibin Yu1, Nan Wang1, Zhaorui Gu1, Bing Zheng1 
TL;DR: This study demonstrated automatic plankton image classification system combining multiple view features using multiple kernel learning using three kernel functions (linear, polynomial and Gaussian kernel functions) can describe and use information of features better so that achieve a higher classification accuracy.
Abstract: Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap. Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system outperforms state-of-the-art plankton image classification systems in terms of accuracy and robustness. This study demonstrated automatic plankton image classification system combining multiple view features using multiple kernel learning. The results indicated that multiple view features combined by NLMKL using three kernel functions (linear, polynomial and Gaussian kernel functions) can describe and use information of features better so that achieve a higher classification accuracy.

88 citations


Cites methods from "Higher-level feature combination vi..."

  • ...[51] proved the effectiveness of MKL for feature combination....

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Journal ArticleDOI
TL;DR: This paper proposes to extract local image-derived biomarkers from DTI and sMRI to construct multimodal AD signatures using a Multiple Kernel Learning (MKL) framework for AD subjects recognition and results indicate that the multimodAL approach yields significant improvement in accuracy over using each single modality independently.

81 citations

Journal ArticleDOI
TL;DR: This paper proposes a novel method for scene-free multi-class weather classification from single images based on multiple category-specific dictionary learning and multiple kernel learning and learns dictionaries based on these features.

46 citations


Cites background from "Higher-level feature combination vi..."

  • ...Multiple kernel learning [15] is viewed as an effective way to fuse features and design an optimal kernel [16,17]....

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Journal ArticleDOI
TL;DR: A multi-task joint sparse and low-rank representation model is introduced to combine the strength of multiple features for HRS image interpretation and achieves remarkable performance and improves upon the state-of-art methods in respective applications.
Abstract: Scene classification plays an important role in the intelligent processing of High-Resolution Satellite (HRS) remotely sensed images. In HRS image classification, multiple features, e.g., shape, color, and texture features, are employed to represent scenes from different perspectives. Accordingly, effective integration of multiple features always results in better performance compared to methods based on a single feature in the interpretation of HRS images. In this paper, we introduce a multi-task joint sparse and low-rank representation model to combine the strength of multiple features for HRS image interpretation. Specifically, a multi-task learning formulation is applied to simultaneously consider sparse and low-rank structures across multiple tasks. The proposed model is optimized as a non-smooth convex optimization problem using an accelerated proximal gradient method. Experiments on two public scene classification datasets demonstrate that the proposed method achieves remarkable performance and improves upon the state-of-art methods in respective applications.

28 citations


Cites background from "Higher-level feature combination vi..."

  • ...Recent studies on Multiple Kernel Learning (MKL) [28] that fuse different features through multiple similarity function combinations can effectively improve the classification performance [29,30]....

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Journal ArticleDOI
TL;DR: The proposed multiple kernel learning (MKL) based on three discriminant features to learn an efficient P300 classifier to improve the accuracy of character recognition in a P300 speller BCI consistently obtains better or similar accuracy in terms of character Recognition.

21 citations


Cites methods from "Higher-level feature combination vi..."

  • ...Multiple kernel learning (MKL), an extension of SVM, has recently been used in an attempt to efficiently handle multiple kernels on classification [19][25][26][28][29][30][32]....

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References
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Proceedings ArticleDOI
20 Jun 2005
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Abstract: We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.

31,952 citations


"Higher-level feature combination vi..." refers methods in this paper

  • ...We experiment our method on two sampling intervals, 6 and 8 pixels, and two low-level descriptors, SIFT [13] and HOG [24], to study its effectiveness and robustness....

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Proceedings ArticleDOI
20 Sep 1999
TL;DR: Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.
Abstract: An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low residual least squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.

16,989 citations


"Higher-level feature combination vi..." refers background or methods in this paper

  • ...We experiment our method on two sampling intervals, 6 and 8 pixels, and two low-level descriptors, SIFT [13] and HOG [24], to study its effectiveness and robustness....

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  • ...For image classification tasks, different descriptors can capture different properties of images and preserve different degrees of discriminative power and invariance, such as PHOG [12] captures shape information while SIFT [13] captures appearance information....

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Proceedings ArticleDOI
17 Jun 2006
TL;DR: This paper presents a method for recognizing scene categories based on approximate global geometric correspondence that exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories.
Abstract: This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting "spatial pyramid" is a simple and computationally efficient extension of an orderless bag-of-features image representation, and it shows significantly improved performance on challenging scene categorization tasks. Specifically, our proposed method exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories. The spatial pyramid framework also offers insights into the success of several recently proposed image descriptions, including Torralba’s "gist" and Lowe’s SIFT descriptors.

8,736 citations


"Higher-level feature combination vi..." refers background or methods in this paper

  • ...6 8 6 8 HV [1] 49....

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  • ...In the framework of SPM [1], a large amount of work contributes to the encoding step....

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  • ...Method SIFT HOG 6 8 6 8 HV [1] 81....

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  • ...We consider three representative encoding methods and two common pooling strategies in our work, namely hard voting (HV) [1], localized soft-assignment coding (LSC) [2], salient coding (SaC) [14] and correspondingly average and max pooling....

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  • ...Datasets and experimental setup We evaluate our method on three image datasets: Caltech-101 [22], UIUC 8-sports [23] and Scene-15 [1]....

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Proceedings ArticleDOI
13 Jun 2010
TL;DR: This paper presents a simple but effective coding scheme called Locality-constrained Linear Coding (LLC) in place of the VQ coding in traditional SPM, using the locality constraints to project each descriptor into its local-coordinate system, and the projected coordinates are integrated by max pooling to generate the final representation.
Abstract: The traditional SPM approach based on bag-of-features (BoF) requires nonlinear classifiers to achieve good image classification performance. This paper presents a simple but effective coding scheme called Locality-constrained Linear Coding (LLC) in place of the VQ coding in traditional SPM. LLC utilizes the locality constraints to project each descriptor into its local-coordinate system, and the projected coordinates are integrated by max pooling to generate the final representation. With linear classifier, the proposed approach performs remarkably better than the traditional nonlinear SPM, achieving state-of-the-art performance on several benchmarks. Compared with the sparse coding strategy [22], the objective function used by LLC has an analytical solution. In addition, the paper proposes a fast approximated LLC method by first performing a K-nearest-neighbor search and then solving a constrained least square fitting problem, bearing computational complexity of O(M + K2). Hence even with very large codebooks, our system can still process multiple frames per second. This efficiency significantly adds to the practical values of LLC for real applications.

3,307 citations

Proceedings ArticleDOI
20 Jun 2009
TL;DR: An extension of the SPM method is developed, by generalizing vector quantization to sparse coding followed by multi-scale spatial max pooling, and a linear SPM kernel based on SIFT sparse codes is proposed, leading to state-of-the-art performance on several benchmarks by using a single type of descriptors.
Abstract: Recently SVMs using spatial pyramid matching (SPM) kernel have been highly successful in image classification. Despite its popularity, these nonlinear SVMs have a complexity O(n2 ~ n3) in training and O(n) in testing, where n is the training size, implying that it is nontrivial to scaleup the algorithms to handle more than thousands of training images. In this paper we develop an extension of the SPM method, by generalizing vector quantization to sparse coding followed by multi-scale spatial max pooling, and propose a linear SPM kernel based on SIFT sparse codes. This new approach remarkably reduces the complexity of SVMs to O(n) in training and a constant in testing. In a number of image categorization experiments, we find that, in terms of classification accuracy, the suggested linear SPM based on sparse coding of SIFT descriptors always significantly outperforms the linear SPM kernel on histograms, and is even better than the nonlinear SPM kernels, leading to state-of-the-art performance on several benchmarks by using a single type of descriptors.

3,017 citations


"Higher-level feature combination vi..." refers background or methods in this paper

  • ...image classification, much work has been contributed to this topic [2, 3, 4, 5, 6, 7]....

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  • ...To make the codes preserve reconstruction ability, the authors in [3] leveraged the sparse coding (ScSPM) technique to encode x....

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  • ...In many encoding methods, the descriptors are encoded independently while not considering their intrinsic relationship [3, 4, 5, 6, 7]....

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