Masayuki Mukunoki

Bio: Masayuki Mukunoki is an academic researcher from University of Miyazaki. The author has contributed to research in topics: CAPTCHA & Sparse approximation. The author has an hindex of 12, co-authored 68 publications receiving 556 citations. Previous affiliations of Masayuki Mukunoki include Kyoto University & Hiroshima City University.

Set based discriminative ranking for recognition

Abstract: Recently both face recognition and body-based person re-identification have been extended from single-image based scenarios to video-based or even more generally image-set based problems. Set-based recognition brings new research and application opportunities while at the same time raises great modeling and optimization challenges. How to make the best use of the available multiple samples for each individual while at the same time not be disturbed by the great within-set variations is considered by us to be the major issue. Due to the difficulty of designing a global optimal learning model, most existing solutions are still based on unsupervised matching, which can be further categorized into three groups: a) set-based signature generation, b) direct set-to-set matching, and c) between-set distance finding. The first two count on good feature representation while the third explores data set structure and set-based distance measurement. The main shortage of them is the lack of learning-based discrimination ability. In this paper, we propose a set-based discriminative ranking model (SBDR), which iterates between set-to-set distance finding and discriminative feature space projection to achieve simultaneous optimization of these two. Extensive experiments on widely-used face recognition and person re-identification datasets not only demonstrate the superiority of our approach, but also shed some light on its properties and application domain.

Shinpuhkan2014: A Multi-Camera Pedestrian Dataset for Tracking People across Multiple Cameras

Abstract: In this paper, we present a public dataset for tracking people across multiple cameras. This dataset consists of more than 22,000 images of 24 people which are captured by 16 cameras installed in a shopping mall “Shinpuh-kan”. All images are manually cropped and resized to 48× 128 pixels, grouped into tracklets and added annotation. The number of tracklets of each person is 86. This dataset contains multiple tracklets in different directions for each person within a camera. To show the difficulty of the dataset, we evaluate it with some state-of-the-art methods.

Common-near-neighbor analysis for person re-identification

Abstract: Person re-identification tackles the problem whether an observed person of interest reappears in a network of cameras. The difficulty primarily originates from few samples per class but large amounts of intra-class variations in real scenarios: illumination, pose and viewpoint changes across cameras. So far, proposals in the literature have treated this either as a matching problem focusing on feature representation or as a classification/ranking problem relying on metric optimization. This paper presents a new way called Common-Near-Neighbor Analysis, which to some extent combines the strengths of these two methodologies. It analyzes the commonness of the near neighbors of each pair of samples in a learned metric space, measured by a novel rank-order based dissimilarity. Our method, using only color cue, has been tested on widely-used benchmark datasets, showing significant performance improvement over the state-of-the-art.

Optimizing Mean Reciprocal Rank for person re-identification

Abstract: Person re-identification is one of the most challenging issues in network-based surveillance. The difficulties mainly come from the great appearance variations induced by illumination, camera view and body pose changes. Maybe influenced by the research on face recognition and general object recognition, this problem is habitually treated as a verification or classification problem, and much effort has been put on optimizing standard recognition criteria. However, we found that in practical applications the users usually have different expectations. For example, in a real surveillance system, we may expect that a visual user interface can show us the relevant images in the first few (e.g. 20) candidates, but not necessarily before all the irrelevant ones. In other words, there is no problem to leave the final judgement to the users. Based on such an observation, this paper treats the re-identification problem as a ranking problem and directly optimizes a listwise ranking function named Mean Reciprocal Rank (MRR), which is considered by us to be able to generate results closest to human expectations. Using a maximum-margin based structured learning model, we are able to show improved re-identification results on widely-used benchmark datasets

Collaboratively Regularized Nearest Points for Set Based Recognition.

Abstract: Set based recognition has been attracting more and more attention in recent years, benefitting from two facts: the difficulty of collecting sets of images for recognition fades quickly, and set based recognition models generally outperform the ones for single instance based recognition. In this paper, we propose a novel model called collaboratively regularized nearest points (CRNP) for solving this problem. The proposal inherits the merits of simplicity, robustness, and high-efficiency from the very recently introduced regularized nearest points (RNP) method on finding the set-to-set distance using the l2-norm regularized affine hulls. Meanwhile, CRNP makes use of the powerful discriminative ability induced by collaborative representation, following the same idea as that in sparse recognition for classification (SRC) for image-based recognition and collaborative sparse approximation (CSA) for set-based recognition. However, CRNP uses l2-norm instead of the expensive l1-norm for coefficients regularization, which makes it much more efficient. Extensive experiments on five benchmark datasets for face recognition and person re-identification demonstrate that CRNP is not only more effective but also significantly faster than other state-of-the-art methods, including RNP and CSA.

Re-ranking Person Re-identification with k-Reciprocal Encoding

Abstract: When considering person re-identification (re-ID) as a retrieval process, re-ranking is a critical step to improve its accuracy. Yet in the re-ID community, limited effort has been devoted to re-ranking, especially those fully automatic, unsupervised solutions. In this paper, we propose a k-reciprocal encoding method to re-rank the re-ID results. Our hypothesis is that if a gallery image is similar to the probe in the k-reciprocal nearest neighbors, it is more likely to be a true match. Specifically, given an image, a k-reciprocal feature is calculated by encoding its k-reciprocal nearest neighbors into a single vector, which is used for re-ranking under the Jaccard distance. The final distance is computed as the combination of the original distance and the Jaccard distance. Our re-ranking method does not require any human interaction or any labeled data, so it is applicable to large-scale datasets. Experiments on the large-scale Market-1501, CUHK03, MARS, and PRW datasets confirm the effectiveness of our method.

Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification

Abstract: Learning generic and robust feature representations with data from multiple domains for the same problem is of great value, especially for the problems that have multiple datasets but none of them are large enough to provide abundant data variations. In this work, we present a pipeline for learning deep feature representations from multiple domains with Convolutional Neural Networks (CNNs). When training a CNN with data from all the domains, some neurons learn representations shared across several domains, while some others are effective only for a specific one. Based on this important observation, we propose a Domain Guided Dropout algorithm to improve the feature learning procedure. Experiments show the effectiveness of our pipeline and the proposed algorithm. Our methods on the person re-identification problem outperform stateof-the-art methods on multiple datasets by large margins.

Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification

Abstract: Learning generic and robust feature representations with data from multiple domains for the same problem is of great value, especially for the problems that have multiple datasets but none of them are large enough to provide abundant data variations. In this work, we present a pipeline for learning deep feature representations from multiple domains with Convolutional Neural Networks (CNNs). When training a CNN with data from all the domains, some neurons learn representations shared across several domains, while some others are effective only for a specific one. Based on this important observation, we propose a Domain Guided Dropout algorithm to improve the feature learning procedure. Experiments show the effectiveness of our pipeline and the proposed algorithm. Our methods on the person re-identification problem outperform state-of-the-art methods on multiple datasets by large margins.

Local Fisher Discriminant Analysis for Pedestrian Re-identification

Abstract: Metric learning methods, for person re-identification, estimate a scaling for distances in a vector space that is optimized for picking out observations of the same individual. This paper presents a novel approach to the pedestrian re-identification problem that uses metric learning to improve the state-of-the-art performance on standard public datasets. Very high dimensional features are extracted from the source color image. A first processing stage performs unsupervised PCA dimensionality reduction, constrained to maintain the redundancy in color-space representation. A second stage further reduces the dimensionality, using a Local Fisher Discriminant Analysis defined by a training set. A regularization step is introduced to avoid singular matrices during this stage. The experiments conducted on three publicly available datasets confirm that the proposed method outperforms the state-of-the-art performance, including all other known metric learning methods. Further-more, the method is an effective way to process observations comprising multiple shots, and is non-iterative: the computation times are relatively modest. Finally, a novel statistic is derived to characterize the Match Characteristic: the normalized entropy reduction can be used to define the 'Proportion of Uncertainty Removed' (PUR). This measure is invariant to test set size and provides an intuitive indication of performance.

Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-identification

Abstract: Most existing person re-identification (re-id) methods require supervised model learning from a separate large set of pairwise labelled training data for every single camera pair. This significantly limits their scalability and usability in real-world large scale deployments with the need for performing re-id across many camera views. To address this scalability problem, we develop a novel deep learning method for transferring the labelled information of an existing dataset to a new unseen (unlabelled) target domain for person re-id without any supervised learning in the target domain. Specifically, we introduce an Transferable Joint Attribute-Identity Deep Learning (TJ-AIDL) for simultaneously learning an attribute-semantic and identity-discriminative feature representation space transferrable to any new (unseen) target domain for re-id tasks without the need for collecting new labelled training data from the target domain (i.e. unsupervised learning in the target domain). Extensive comparative evaluations validate the superiority of this new TJ-AIDL model for unsupervised person re-id over a wide range of state-of-the-art methods on four challenging benchmarks including VIPeR, PRID, Market-1501, and DukeMTMC-ReID.