Kinds Of Local Features Research Articles

Local Heterogeneous Features for Person Re-Identification in Harsh Environments

Local features could learn semantic information for pedestrian images and they are very important for person re-identification (Re-ID) in harsh environments. However, most approaches only optimize one kind of local feature, which results in incomplete local features. In this paper, we propose Local Heterogeneous Features (LHF) to extract discriminative local features from three aspects. To this end, we utilize three kinds of losses to learn three kinds of local features, i.e., local discriminative features, local relative features, local compact features. As for local discriminative features, we split the attention maps into three horizontal sub-regions and perform the classification operation. Then, we divide the attention maps into two horizontal sub-regions, and we synchronously apply the triplet loss and center loss to learn local relative features and local compact features. Finally, we utilize local discriminative features to represent pedestrian. We evaluate LHF on public person Re-ID datasets and prove LHF is meaningful for local feature learning.

Coarse-Fine Convolutional Neural Network for Person Re-Identification in Camera Sensor Networks

In this paper, we present a novel deep model named coarse-fine convolutional neural network (CFCNN) for person re-identification in camera sensor networks, which jointly learns global and multi-scale local features simultaneously. To this end, we design the CFCNN as a multi-branch network, which is composed of one coarse and two fine branches. Specifically, the global feature is learned from the coarse branch, and the two fine branches are developed to extract two kinds of local features with different scales. Afterward, each branch is followed by a classification loss to make the identity prediction. Finally, we obtain completed pedestrian representations via concatenating the learned global and all local features. We conduct a number of experiments to evaluate the effectiveness of the CFCNN on three datasets. The CFCNN achieves high rank-1 and mAP accuracy with 94.0%/81.2%, 64.6%/58.4%, and 85.7%/72.4% on Market-1501, CUHK03, and DukeMTMC-reID, respectively. These results significantly outperform the prior state-of-the-art methods.

Cross-Domain Ground-Based Cloud Classification Based on Transfer of Local Features and Discriminative Metric Learning

Cross-domain ground-based cloud classification is a challenging issue as the appearance of cloud images from different cloud databases possesses extreme variations. Two fundamental problems which are essential for cross-domain ground-based cloud classification are feature representation and similarity measurement. In this paper, we propose an effective feature representation called transfer of local features (TLF), and measurement method called discriminative metric learning (DML). The TLF is a generalized representation framework that can integrate various kinds of local features, e.g., local binary patterns (LBP), local ternary patterns (LTP) and completed LBP (CLBP). In order to handle domain shift, such as variations of illumination, image resolution, capturing location, occlusion and so on, the TLF mines the maximum response in regions to make a stable representation for domain variations. We also propose to learn a discriminant metric, simultaneously. We make use of sample pairs and the relationship among cloud classes to learn the distance metric. Furthermore, in order to improve the practicability of the proposed method, we replace the original cloud images with the convolutional activation maps which are then applied to TLF and DML. The proposed method has been validated on three cloud databases which are collected in China alone, provided by Chinese Academy of Meteorological Sciences (CAMS), Meteorological Observation Centre (MOC), and Institute of Atmospheric Physics (IAP). The classification accuracies outperform the state-of-the-art methods.

Classification of brain disease in magnetic resonance images using two-stage local feature fusion.

BackgroundMany classification methods have been proposed based on magnetic resonance images. Most methods rely on measures such as volume, the cerebral cortical thickness and grey matter density. These measures are susceptible to the performance of registration and limited in representation of anatomical structure. This paper proposes a two-stage local feature fusion method, in which deformable registration is not desired and anatomical information is represented from moderate scale.MethodsKeypoints are firstly extracted from scale-space to represent anatomical structure. Then, two kinds of local features are calculated around the keypoints, one for correspondence and the other for representation. Scores are assigned for keypoints to quantify their effect in classification. The sum of scores for all effective keypoints is used to determine which group the test subject belongs to.ResultsWe apply this method to magnetic resonance images of Alzheimer's disease and Parkinson's disease. The advantage of local feature in correspondence and representation contributes to the final classification. With the help of local feature (Scale Invariant Feature Transform, SIFT) in correspondence, the performance becomes better. Local feature (Histogram of Oriented Gradient, HOG) extracted from 16×16 cell block obtains better results compared with 4×4 and 8×8 cell block.DiscussionThis paper presents a method which combines the effect of SIFT descriptor in correspondence and the representation ability of HOG descriptor in anatomical structure. This method has the potential in distinguishing patients with brain disease from controls.

Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice

Video based action recognition is one of the important and challenging problems in computer vision research. Bag of visual words model (BoVW) with local features has been very popular for a long time and obtained the state-of-the-art performance on several realistic datasets, such as the HMDB51, UCF50, and UCF101. BoVW is a general pipeline to construct a global representation from local features, which is mainly composed of five steps; (i) feature extraction, (ii) feature pre-processing, (iii) codebook generation, (iv) feature encoding, and (v) pooling and normalization. Although many efforts have been made in each step independently in different scenarios, their effects on action recognition are still unknown. Meanwhile, video data exhibits different views of visual patterns , such as static appearance and motion dynamics. Multiple descriptors are usually extracted to represent these different views. Fusing these descriptors is crucial for boosting the final performance of an action recognition system. This paper aims to provide a comprehensive study of all steps in BoVW and different fusion methods, and uncover some good practices to produce a state-of-the-art action recognition system. Specifically, we explore two kinds of local features, ten kinds of encoding methods, eight kinds of pooling and normalization strategies, and three kinds of fusion methods. We conclude that every step is crucial for contributing to the final recognition rate and improper choice in one of the steps may counteract the performance improvement of other steps. Furthermore, based on our comprehensive study, we propose a simple yet effective representation, called hybrid supervector, by exploring the complementarity of different BoVW frameworks with improved dense trajectories. Using this representation, we obtain impressive results on the three challenging datasets; HMDB51 (61.9%), UCF50 (92.3%), and UCF101 (87.9%).

Rotationally Invariant Descriptors Using Intensity Order Pooling

This paper proposes a novel method for interest region description which pools local features based on their intensity orders in multiple support regions. Pooling by intensity orders is not only invariant to rotation and monotonic intensity changes, but also encodes ordinal information into a descriptor. Two kinds of local features are used in this paper, one based on gradients and the other on intensities; hence, two descriptors are obtained: the Multisupport Region Order-Based Gradient Histogram (MROGH) and the Multisupport Region Rotation and Intensity Monotonic Invariant Descriptor (MRRID). Thanks to the intensity order pooling scheme, the two descriptors are rotation invariant without estimating a reference orientation, which appears to be a major error source for most of the existing methods, such as Scale Invariant Feature Transform (SIFT), SURF, and DAISY. Promising experimental results on image matching and object recognition demonstrate the effectiveness of the proposed descriptors compared to state-of-the-art descriptors.

Evaluation of local features and classifiers in BOW model for image classification

Bag-of-word (BOW) is used in many state-of-the-art methods of image classification, and it is especially suitable for multi-class classification. Many kinds of local features and classifiers are applicable for the BOW model. However, it is unclear which kind of local feature is the most distinctive and meanwhile robust, and which classifier can optimize classification performance. In this paper, we discuss the implementation choices in the BOW model. Further, we evaluate the influences of local features and classifiers on object and texture recognition methods in the framework of the BOW model. To evaluate the implementation choices, we use two popular datasets: the Xerox7 dataset and the UIUCTex dataset. Extensive experiments are carried out to compare the performance of different detectors, descriptors and classifiers in term of classification accuracy on the object category dataset and the texture dataset. We find that the combinational detector which combines the MSER detector with the Hessian-Laplacian detector is efficient to find discriminative regions. We also find that the SIFT descriptor performs better than the other descriptors for image classification, and that the SVM classifier with the EMD kernel is superior to other classifiers. More than that, we propose an EMD spatial kernel to encode the spatial information of local features. The EMD spatial kernel is implemented on the Xerox7 dataset, the 4-class VOC2006 dataset and the 4-class Caltech101 dataset. The experimental results show that the proposed kernel outperforms the EMD kernel which does not consider the spatial information in image classification.

Phase congruency induced local features for finger-knuckle-print recognition

Researchers have recently found that the finger-knuckle-print (FKP), which refers to the inherent skin patterns of the outer surface around the phalangeal joint of one’s finger, has high discriminability, making it an emerging promising biometric identifier. Effective feature extraction and matching plays a key role in such an FKP based personal authentication system. This paper studies image local features induced by the phase congruency model, which is supported by strong psychophysical and neurophysiological evidences, for FKP recognition. In the computation of phase congruency, the local orientation and the local phase can also be defined and extracted from a local image patch. These three local features are independent of each other and reflect different aspects of the image local information. We compute efficiently the three local features under the computation framework of phase congruency using a set of quadrature pair filters. We then propose to integrate these three local features by score-level fusion to improve the FKP recognition accuracy. Such kinds of local features can also be naturally combined with Fourier transform coefficients, which are global features. Experiments are performed on the PolyU FKP database to validate the proposed FKP recognition scheme.