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IEEE Transactions on Pattern Analysis and Machine Intelligence

Alexander Shekhovtsov, Paul Swoboda, Bogdan Savchynskyy
We consider the NP-hard problem of MAP-inference for undirected discrete graphical models. We propose a polynomial time and practically efficient algorithm for finding a part of its optimal solution. Specifically, our algorithm marks some labels of the considered graphical model either as (i) optimal, meaning that they belong to all optimal solutions of the inference problem; (ii) non-optimal if they provably do not belong to any solution. With access to an exact solver of a linear programming relaxation to the MAP-inference problem, our algorithm marks the maximal possible (in a specified sense) number of labels...
July 24, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Sune Darkner, Akshay Pai, Matthew G Liptrot, Jon Sporring
Diffeomorphic deformation is a popular choice in medical image registration. A fundamental property of diffeomorphisms is in vertibility, implying that once the relation between two points A to B is found, then the relation B to A is given per definition. Consistency is a measure of a numerical algorithm's ability to mimic this invertibility, and achieving consistency has proven to be a challenge for many state-of-the-art algorithms.
July 21, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Yu Zhang, Xiaowu Chen, Jia Li, Chen Wang, Changqun Xia, Jun Li
Semantic object segmentation (SOS) is a challenging task in computer vision that aims to detect and segment all pixels of the objects within predefined semantic categories. In image-based SOS, many supervised models have been proposed and achieved impressive performances due to the rapid advances of well-annotated training images and machine learning theories. However, in video-based SOS it is often difficult to directly train a supervised model since most videos are weakly annotated by tags. To handle such tagged videos, this paper proposes a novel approach that adopts a segmentation-by-detection framework...
July 20, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Dan Xie, Tianmin Shu, Sinisa Todorovic, Song-Chun Zhu
This paper presents a method for localizing functional objects and predicting human intents and trajectories in surveillance videos of public spaces, under no supervision in training. People in public spaces are expected to intentionally take shortest paths (subject to obstacles) toward certain objects (e.g. vending machine, picnic table, dumpster etc.) where they can satisfy certain needs (e.g., quench thirst). Since these objects are typically very small or heavily occluded, they cannot be inferred by their visual appearance but indirectly by their influence on people's trajectories...
July 19, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Xi Yin, Xiaoming Liu, Jin Chen, David M Kramer
This paper proposes a novel framework for fluorescence plant video processing. The plant research community is interested in the leaf-level photosynthetic analysis within a plant. A prerequisite for such analysis is to segment all leaves, estimate their structures, and track them over time. We identify this as a joint multi-leaf segmentation, alignment, and tracking problem. First, leaf segmentation and alignment are applied on the last frame of a plant video to find a number of well-aligned leaf candidates...
July 17, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Yi Wang, Jianwu Wan, Jun Guo, Yiu-Ming Cheung, Pong C Yuen
Similarity search is essential to many important applications and often involves searching at scale on high-dimensional data based on their similarity to a query. In biometric applications, recent vulnerability studies have shown that adversarial machine learning can compromise biometric recognition systems by exploiting the biometric similarity information. Existing methods for biometric privacy protection are in general based on pairwise matching of secured biometric templates and have inherent limitations in search efficiency and scalability...
July 14, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Xin Chen, Jian Weng, Wei Lu, Jiaming Xu
Gait recognition is an important topic in biometrics. Current works primarily focus on recognizing a single person's walking gait. However, a person's gait will change when they walk with other people. How to recognize the gait of multiple people walking is still a challenging problem. This paper proposes an attribute discovery model in a max-margin framework to recognize a person based on gait while walking with multiple people. First, human graphlets are integrated into a tracking-bydetection method to obtain a person's complete silhouette...
July 12, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Syed Zulqarnain Gilani, Ajmal Mian, Faisal Shafait, Ian Reid
We present an algorithm that automatically establishes dense correspondences between a large number of 3D faces. Starting from automatically detected sparse correspondences on the outer boundary of 3D faces, the algorithm triangulates existing correspondences and expands them iteratively by matching points of distinctive surface curvature along the triangle edges. After exhausting keypoint matches, further correspondences are established by generating evenly distributed points within triangles by evolving level set geodesic curves from the centroids of large triangles...
July 11, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Bumsub Ham, Minsu Cho, Cordelia Schmid, Jean Ponce
Finding image correspondences remains a challenging problem in the presence of intra-class variations and large changes in scene layout. Semantic flow methods are designed to handle images depicting different instances of the same object or scene category. We introduce a novel approach to semantic flow, dubbed proposal flow, that establishes reliable correspondences using object proposals. Unlike prevailing semantic flow approaches that operate on pixels or regularly sampled local regions, proposal flow benefits from the characteristics of modern object proposals, that exhibit high repeatability at multiple scales, and can take advantage of both local and geometric consistency constraints among proposals...
July 11, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Davide Modolo, Vittorio Ferrari
We propose a technique to train semantic part-based models of object classes from Google Images. Our models encompass the appearance of parts and their spatial arrangement on the object, specific to each viewpoint. We learn these rich models by collecting training instances for both parts and objects, and automatically connecting the two levels. Our framework works incrementally, by learning from easy examples first, and then gradually adapting to harder ones. A key benefit of this approach is that it requires no manual part location annotations...
July 7, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Yu Zhang, Mao Ye, Dinesh Manocha, Ruigang Yang
We present a practical and inexpensive method to reconstruct 3D scenes that include transparent and mirror objects. Our work is motivated by the need for automatically generating 3D models of interior scenes, which commonly include glass. These large structures are often invisible to cameras. Existing 3D reconstruction methods for transparent objects are usually not applicable in such a room-sized reconstruction setting. Our simple hardware setup augments a regular depth camera with a single ultrasonic sensor, which is able to measure the distance to any object, including transparent surfaces...
July 6, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Peixi Peng, Yonghong Tian, Tao Xiang, Yaowei Wang, Massimiliano Pontil, Tiejun Huang
A number of vision problems such as zero-shot learning and person re-identification can be considered as cross-class transfer learning problems. As mid-level semantic properties shared cross different object classes, attributes have been studied extensively for knowledge-transfer across classes. Most previous attribute learning methods focus only on human-defined/nameable semantic attributes, whilst ignoring the fact there also exist undefined/latent shareable visual properties, or latent attributes. These latent attributes can be either discriminative or non-discriminative parts depending on whether they can contribute to an object recognition task...
July 6, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Ching-Hui Chen, Vishal M Patel, Rama Chellappa
Learning a classifier from ambiguously labeled face images is challenging since training images are not always explicitly-labeled. For instance, face images of two persons in a news photo are not explicitly labeled by their names in the caption. We propose a Matrix Completion for Ambiguity Resolution (MCar) method for predicting the actual labels from ambiguously labeled images. This step is followed by learning a standard supervised classifier from the disambiguated labels to classify new images. To prevent the majority labels from dominating the result of MCar, we generalize MCar to a weighted MCar (WMCar) that handles label imbalance...
July 4, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Tsung-Yu Lin, Aruni RoyChowdhury, Subhransu Maji
We present a simple and effective architecture for fine-grained recognition called Bilinear Convolutional Neural Networks (B-CNNs). These networks represent an image as a pooled outer product of features derived from two CNNs and capture localized feature interactions in a translationally invariant manner. B-CNNs are related to orderless texture representations built on deep features but can be trained in an end-to-end manner. Our most accurate model obtains 84.1%, 79.4%, 84.5% and 91.3% per-image accuracy on the Caltech-UCSD birds [66], NABirds [63], FGVC aircraft [42], and Stanford cars [33] dataset respectively and runs at 30 frames-per-second on a NVIDIA Titan X GPU...
July 4, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, Antonio Torralba
The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach near-human semantic classification performance at tasks such as visual object and scene recognition. Here we describe the Places Database, a repository of 10 million scene photographs, labeled with scene semantic categories, comprising a large and diverse list of the types of environments encountered in the world. Using the state-of-the-art Convolutional Neural Networks (CNNs), we provide scene classification CNNs (Places-CNNs) as baselines, that significantly outperform the previous approaches...
July 4, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Arnau Ramisa, Fei Yan, Francesc Moreno-Noguer, Krystian Mikolajczyk
Current approaches lying in the intersection of computer vision and NLP have achieved unprecedented breakthroughs in tasks like automatic captioning or image retrieval. Most of these methods, though, rely on training sets of images associated with annotations that specifically describe the visual content. This paper proposes going a step further and explores more complex cases where textual descriptions are loosely related to images. We focus on the particular domain of News. We introduce new deep learning methods that address source and popularity prediction, article illustration, and article geolocation...
June 30, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Dimitrios Georgios Evangelidis, Radu Horaud
This paper addresses the problem of registering multiple point sets. Solutions to this problem are often approximated by repeatedly solving for pairwise registration, which results in an uneven treatment of the sets forming a pair: a model set and a data set. The main drawback of this strategy is that the model set may contain noise and outliers, which negatively affects the estimation of the registration parameters. In contrast, the proposed formulation treats all the point sets on an equal footing. Indeed, all the points are drawn from a central Gaussian mixture, hence the registration is cast into a clustering problem...
June 21, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Mingliang Chen, Xing Wei, Qingxiong Yang, Qing Li, Gang Wang, Ming-Hsuan Yang
We propose a background subtraction algorithm using hierarchical superpixel segmentation, spanning trees and optical flow. First, we generate superpixel segmentation trees using a number of Gaussian Mixture Models (GMMs) by treating each GMM as one vertex to construct spanning trees. Next, we use the M-smoother to enhance the spatial consistency on the spanning trees and estimate optical flow to extend the M-smoother to the temporal domain. Experimental results on synthetic and real-world benchmark datasets show that the proposed algorithm performs favorably for background subtraction in videos against the state-of-the-art methods in spite of frequent and sudden changes of pixel values...
June 21, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Tao Wang, Haibin Ling
Matching-based algorithms have been commonly used in planar object tracking. They often model a planar object as a set of keypoints, and then find correspondences between keypoint sets via descriptor matching. In previous work, unary constraints on appearances or locations are usually used to guide the matching. However, these approaches rarely utilize structure information of the object, and are thus suffering from various perturbation factors. In this paper, we proposed a graph-based tracker, named Gracker, which is able to fully explore the structure information of the object to enhance tracking performance...
June 16, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Tingting Mu, Yannis John Goulermas, Sophia Ananiadou
A typical objective of data visualization is to generate low-dimensional plots that maximally convey the information within the data. The visualization output should help the user to not only identify the local neighborhood structure of individual samples, but also obtain a global view of the relative positioning and separation between cohorts. Here, we propose a very novel visualization framework designed to satisfy these needs. By incorporating additional cohort positioning and discriminative constraints into local neighbor preservation models through the use of computed cohort prototypes, effective control over the arrangements and proximities of data cohorts can be obtained...
June 15, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
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