From BoW to CNN: Two Decades of Texture Representation for Texture Classification

Liu, Li; Chen, Jie; Fieguth, Paul; Zhao, Guoying; Chellappa, Rama; Pietikäinen, Matti

doi:10.1007/s11263-018-1125-z

Cited by 319 publications

(223 citation statements)

References 236 publications

(599 reference statements)

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“…There are basically four components for a conventional biometric system: preprocessing, feature extraction, matching, and decision phase. The feature extraction method affects the performance of the system significantly; there are many feature extraction techniques described in [22]. This paper proposes a multimodal biometric system based on the face and iris, which uses a multi-resolution 2D Log-Gabor filter with spectral regression kernel discriminant analysis (SRKDA) to extract pertinent features from the iris.…”

Section: Introductionmentioning

confidence: 99%

Face–Iris Multimodal Biometric Identification System

et al. 2020

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Multimodal biometrics technology has recently gained interest due to its capacity to overcome certain inherent limitations of the single biometric modalities and to improve the overall recognition rate. A common biometric recognition system consists of sensing, feature extraction, and matching modules. The robustness of the system depends much more on the reliability to extract relevant information from the single biometric traits. This paper proposes a new feature extraction technique for a multimodal biometric system using face–iris traits. The iris feature extraction is carried out using an efficient multi-resolution 2D Log-Gabor filter to capture textural information in different scales and orientations. On the other hand, the facial features are computed using the powerful method of singular spectrum analysis (SSA) in conjunction with the wavelet transform. SSA aims at expanding signals or images into interpretable and physically meaningful components. In this study, SSA is applied and combined with the normal inverse Gaussian (NIG) statistical features derived from wavelet transform. The fusion process of relevant features from the two modalities are combined at a hybrid fusion level. The evaluation process is performed on a chimeric database and consists of Olivetti research laboratory (ORL) and face recognition technology (FERET) for face and Chinese academy of science institute of automation (CASIA) v3.0 iris image database (CASIA V3) interval for iris. Experimental results show the robustness.

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Section: Introductionmentioning

confidence: 99%

Face–Iris Multimodal Biometric Identification System

et al. 2020

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“…With VLAD able to capture local shape information, we turn to image texture features as a way to capture differences in surface appearances. The computer vision literature is full of methods for capturing image texture features [16]. The technique of Binarized Statistical Image Features (BSIF) [17] is a relatively recent and robust algorithm for separating distinct textures.…”

Section: Computer Visionmentioning

confidence: 99%

Predicting compressive strength of consolidated molecular solids using computer vision and deep learning

et al. 2020

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We explore the application of computer vision and machine learning (ML) techniques to predict material properties (e.g., compressive strength) based on SEM images of material microstructure. We show that it is possible to train ML models to predict materials performance based on SEM images alone, demonstrating this capability on the real-world problem of predicting uniaxially compressed peak stress of consolidated molecular solids (i.e. TATB) samples. Our image-based ML approach reduces root mean square error (RMSE) by an average of 51% over a non-image-based baseline. We compared two complementary approaches to this problem: (1) a traditional ML approach, random forest (RF), using state-ofthe-art computer vision features and (2) an end-to-end deep learning (DL) approach, where features are learned automatically from raw images. We demonstrate the complementarity of these approaches, showing that RF performs best in the "small data" regime in which many real-world scientific applications reside (up to 24% lower RMSE than DL), whereas DL outpaces RF in the "big data" regime, where abundant training samples are available (up to 24% lower RMSE than RF).

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“…e higher accuracy indoor scene classification effect will be achieved by the spatial 3D information contained in the depth image, which is insensitive to light and reflects the position relationship between objects. Features of the original images will be extracted by D-SIFT (Dense SIFT) [32], and similar features will be clustered to form BoW (Bag-of-Words) [33][34][35] by K-means [36,37]. Based on BoW, the initial descriptors set including visual image descriptors and depth image descriptors will be generated with the construction of SPM.…”

Section: Multiple Image Descriptor Generation and Filteringmentioning

confidence: 99%

Research on Indoor Scene Classification Mechanism Based on Multiple Descriptors Fusion

Qin

Pan

et al. 2020

Mobile Information Systems

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This study aims at the great limitations caused by the non-ROI (region of interest) information interference in traditional scene classification algorithms, including the changes of multiscale or various visual angles and the high similarity between classes and other factors. An effective indoor scene classification mechanism based on multiple descriptors fusion is proposed, which introduces the depth images to improve descriptor efficiency. The greedy descriptor filter algorithm (GDFA) is proposed to obtain valuable descriptors, and the multiple descriptor combination method is also given to further improve descriptor performance. Performance analysis and simulation results show that multiple descriptors fusion not only can achieve higher classification accuracy than principal components analysis (PCA) in the condition with medium and large size of descriptors but also can improve the classification accuracy than the other existing algorithms effectively.

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From BoW to CNN: Two Decades of Texture Representation for Texture Classification

Cited by 319 publications

References 236 publications

Face–Iris Multimodal Biometric Identification System

Face–Iris Multimodal Biometric Identification System

Predicting compressive strength of consolidated molecular solids using computer vision and deep learning

Research on Indoor Scene Classification Mechanism Based on Multiple Descriptors Fusion

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