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A feature fusion based localized multiple kernel learning system for real world image classification

EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING 2017年第1期2017卷 1-11页

作者： Zamani, Fatemeh Jamzad, Mansour Sharif Univ Technol Dept Comp Engn Tehran Iran

Real-world image classification, which aims to determine the semantic class of un-labeled images, is a challenging task. In this paper, we focus on two challenges of image classification and propose a method to address both of them simultaneously. The first challenge is that representing images by heterogeneous features, such as color, shape and texture, helps to provide better classification accuracy. The second challenge comes from dissimilarities in the visual appearance of images from the same class (intra class variance) and similarities between images from different classes (inter class relationship). In addition to these two challenges, we should note that the feature space of real-world images is highly complex so they cannot be linearly classified. The kernel trick is efficacious to classify them. This paper proposes a feature fusion based multiple kernel learning (MKL) model for image classification. By using multiple kernels extracted from multiple features, we address the first challenge. To provide a solution for the second challenge, we use the idea of a localized MKL by assigning separate local weights to each kernel. We employed spatial pyramid match (SPM) representation of images and computed kernel weights based on chi(2)kernel. Experimental results demonstrate that our proposed model has achieved promising results.

关键词： Image classification Spatial pyramid matching localized multiple kernel learning kernel local weighting Feature fusion

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L_p Norm localized multiple kernel learning via Semi-Definite Programming

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IEEE SIGNAL PROCESSING LETTERS 2012年第10期19卷 688-691页

作者： Han, Yina Yang, Kunde Liu, Guizhong Northwestern Polytech Univ Sch Marine Engn Xian 710072 Peoples R China Chinese Acad Sci Inst Acoust State Key Lab Acoust Beijing 100190 Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China

Our objective is to train SVM based localized multiple kernel learning with arbitrary l(p)-norm constraint using the alternating optimization between the standard SVM solvers with the localized combination of base kernels and associated sample-specific kernel weights. Unfortunately, the latter forms a difficult l(p)-norm constraint quadratic optimization. In this letter, by approximating the l(p)-norm using Taylor expansion, the problem of updating the localized kernel weights is reformulated as a non-convex quadratically constraint quadratic programming, and then solved via associated convex Semi-Definite Programming relaxation. Experiments on ten benchmark machine learning datasets demonstrate the advantages of our approach.

关键词： localized multiple kernel learning semi-definite programming support vector machine

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localized multiple kernel Support Vector Data Description 18

Localized Multiple Kernel Support Vector Data Description

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18th IEEE International Conference on Data Mining Workshops (ICDMW)

作者： Gautam, Chandan Tiwari, Aruna Indian Inst Technol Indore Discipline Comp Sci & Engn Simrol India

ISBN: (纸本)9781538692882

In the last decade, SVM-based one-class classifier is well explored for anomaly detection using single-kernel learning. Recently, it is also explored for multiple-kernel learning using a fixed combination of weight where same weight is assigned to each kernel over the complete input space. In this paper, an SVM-based one-class classifier (i.e. Support Vector Data Descriptor (SV DD)) is adapted for localized multi-kernel learning and referred to LMSV DD. Here, the present locality in the input space is considered as the deciding factor to assign different weights to a kernel for different regions of the input space. This localization has been achieved by using a gating function which is trained in tandem with an SV DD-based one-class classifier. Localization also helps in achieving the sparser solution compared to existing multi-kernel-based method as it uses less number of support vectors in many cases. The performance is evaluated based on the extensive experiment over 23 benchmark datasets from various disciplines and compared LMSV DD with 6 state-of-the-art kernel-based methods. LMSV DD outperformed existing single and multi-kernel based methods and the results have been also statistically verified using a Friedman test.

关键词： One-Class Classification Anomaly Detection SVM SV DD localized multiple kernel learning

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Success based locally weighted multiple kernel combination

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PATTERN RECOGNITION 2017年 68卷 38-51页

作者： Kannao, Raghvendra Guha, Prithwijit Indian Inst Technol Guwahati Gauhati 781039 Assam India

multiple kernel learning (MKL) literature has mostly focused on learning weights for base kernel combiners. Recent works using instance dependent weights have resulted in better performance compared to fixed weight MKL approaches. This may be attributed to the fact that, different base kernels have varying discriminative capabilities in distinct local regions of input space. We refer to the zones of classification expertize of base kernels as their "Regions of Success" (RoS). We propose to identify and model them (during training) through a set of instance dependent success prediction functions (SPF) having high values in RoS (and low, otherwise). During operation, the use of these SPFs as instance dependent weighing functions promotes locally discriminative base kernels while suppressing others. We have experimented with 21 benchmark datasets from various domains having large variations in terms of dataset size, interclass imbalances and number of features. Our proposal has achieved higher classification rates and balanced performance (for both positive and negative classes) compared to other instance dependent and fixed weight approaches. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Support vector machine multiple kernel learning kernel alignment localized multiple kernel learning Regions of success Success prediction functions Feature selection kernel selection Feature fusion Support vector regression

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Biologically inspired task oriented gist model for scene classification

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COMPUTER VISION AND IMAGE UNDERSTANDING 2013年第1期117卷 76-95页

作者： Han, Yina Liu, Guizhong Northwestern Polytech Univ Sch Marine Engn Xian 710072 Peoples R China Chinese Acad Sci Inst Acoust State Key Lab Acoust Beijing 100190 Peoples R China Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China

Capturing the scene gist is account for rapid and accurate scene classification in human visual system. This paper presents a biologically inspired task oriented gist model (BT-Gist) that attempts to emulate two important attributes of biological gist: holistic scene centered spatial layout representation and task oriented resolution determination. For the first attribute, we enrich the model of Oliva and Torralba by refining the low-level features in several biological plausible ways, extending the spatial layout to multiple resolution and followed by perceptually meaningful manifold analysis for a set of multi-resolution biologically inspired intrinsic manifold spatial layouts (BMSLs). Since the optimal resolution that best represents the spatial layout varies from task to task, we embody the second attribute as learning the combination of BMSLs of multiple resolution with respect to their optimal discriminative invariance trade-off for the task at hand, and then cast it in the SVM based localized multiple kernel learning (LMKL) framework, by which the kernel of each scene gist is approximated as a local combination of kernels associated to multi-resolution BMSLs. By exploring the task specific category distribution pattern over BMSL, we define the local model as a category distribution sensitive (CDS) kernel, which can accommodate both the diverse individuality of specific BMSL and the universality shared within the whole category space. Via CDS-LMKL, both the optimal resolution for spatial layouts and the final classifier can be efficiently obtained in a joint manner. We evaluate BT-Gist on four natural scene databases and one cluttered indoor scene database with a range of comparison: From different MKL methods, to various biologically inspired models and BoF based computer vision models. CDS-LMKL leads to better results compared to several existing MKL algorithms. Given the two biological attributes that the framework has to follow, BT-Gist, despite its holist

关键词： Scene classification Scene gist localized multiple kernel learning SVM

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Efficient learning of Sample-Specific Discriminative Features for Scene Classification

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IEEE SIGNAL PROCESSING LETTERS 2011年第11期18卷 683-686页

作者： Han, Yina Liu, Guizhong Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China

learning the sample-specific discriminative features based on numerous local learning may not scale well to real world scene classification tasks and suffer from the risk of overfitting. Hence we cast it in SVM based localized multiple kernel learning framework, and design a new strategy to alternately optimize the standard SVM solver and the sample-specific kernel weights, by either a linear programming (for l(1) - norm) or with closed-form solutions (for l(p) - norm). Experiments on both natural scene dataset and cluttered indoor scene dataset demonstrate the effectiveness and efficiency of our approach.

关键词： localized multiple kernel learning scene classification support vector machine

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