Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

Multi-class Heterogeneous Domain Adaptation

© 2019 Joey Tianyi Zhou, Ivor W. Tsang, Sinno Jialin Pan, Mingkui Tan. A crucial issue in heterogeneous domain adaptation (HDA) is the ability to learn a feature mapping between different types of features across domains. Inspired by language translation, a word translated from one language corresponds to only a few words in another language, we present an efficient method named Sparse Heterogeneous Feature Representation (SHFR) in this paper for multi-class HDA to learn a sparse feature transformation between domains with multiple classes. Specifically, we formulate the problem of learning the feature transformation as a compressed sensing problem by building multiple binary classifiers in the target domain as various measurement sensors, which are decomposed from the target multi-class classification problem. We show that the estimation error of the learned transformation decreases with the increasing number of binary classifiers. In other words, for adaptation across heterogeneous domains to be successful, it is necessary to construct a sufficient number of incoherent binary classifiers from the original multi-class classification problem. To achieve this, we propose to apply the error correcting output correcting (ECOC) scheme to generate incoherent classifiers. To speed up the learning of the feature transformation across domains, we apply an efficient batch-mode algorithm to solve the resultant nonnegative sparse recovery problem. Theoretically, we present a generalization error bound of our proposed HDA method under a multi-class setting. Lastly, we conduct extensive experiments on both synthetic and real-world datasets to demonstrate the superiority of our proposed method over existing state-of-the-art HDA methods in terms of prediction accuracy and training efficiency

Transfer hashing with privileged information

Most existing learning to hash methods assume that there are sufficient data, either labeled or unlabeled, on the domain of interest (i.e., the target domain) for training. However, this assumption cannot be satisfied in some real-world applications. To address this data sparsity issue in hashing, inspired by transfer learning, we propose a new framework named Transfer Hashing with Privileged Information (THPI). Specifically, we extend the standard learning to hash method, Iterative Quantization (ITQ), in a transfer learning manner, namely ITQ+. In ITQ+, a new slack function is learned from auxiliary data to approximate the quantization error in ITQ. We developed an alternating optimization approach to solve the resultant optimization problem for ITQ+. We further extend ITQ+ to LapITQ+ by utilizing the geometry structure among the auxiliary data for learning more precise binary codes in the target domain. Extensive experiments on several benchmark datasets verify the effectiveness of our proposed approaches through comparisons with several state-of-the-art baselines

Scene Monitoring With A Forest Of Cooperative Sensors

In this dissertation, we present vision based scene interpretation methods for monitoring of people and vehicles, in real-time, within a busy environment using a forest of co-operative electro-optical (EO) sensors. We have developed novel video understanding algorithms with learning capability, to detect and categorize people and vehicles, track them with in a camera and hand-off this information across multiple networked cameras for multi-camera tracking. The ability to learn prevents the need for extensive manual intervention, site models and camera calibration, and provides adaptability to changing environmental conditions. For object detection and categorization in the video stream, a two step detection procedure is used. First, regions of interest are determined using a novel hierarchical background subtraction algorithm that uses color and gradient information for interest region detection. Second, objects are located and classified from within these regions using a weakly supervised learning mechanism based on co-training that employs motion and appearance features. The main contribution of this approach is that it is an online procedure in which separate views (features) of the data are used for co-training, while the combined view (all features) is used to make classification decisions in a single boosted framework. The advantage of this approach is that it requires only a few initial training samples and can automatically adjust its parameters online to improve the detection and classification performance. Once objects are detected and classified they are tracked in individual cameras. Single camera tracking is performed using a voting based approach that utilizes color and shape cues to establish correspondence in individual cameras. The tracker has the capability to handle multiple occluded objects. Next, the objects are tracked across a forest of cameras with non-overlapping views. This is a hard problem because of two reasons. First, the observations of an object are often widely separated in time and space when viewed from non-overlapping cameras. Secondly, the appearance of an object in one camera view might be very different from its appearance in another camera view due to the differences in illumination, pose and camera properties. To deal with the first problem, the system learns the inter-camera relationships to constrain track correspondences. These relationships are learned in the form of multivariate probability density of space-time variables (object entry and exit locations, velocities, and inter-camera transition times) using Parzen windows. To handle the appearance change of an object as it moves from one camera to another, we show that all color transfer functions from a given camera to another camera lie in a low dimensional subspace. The tracking algorithm learns this subspace by using probabilistic principal component analysis and uses it for appearance matching. The proposed system learns the camera topology and subspace of inter-camera color transfer functions during a training phase. Once the training is complete, correspondences are assigned using the maximum a posteriori (MAP) estimation framework using both the location and appearance cues. Extensive experiments and deployment of this system in realistic scenarios has demonstrated the robustness of the proposed methods. The proposed system was able to detect and classify targets, and seamlessly tracked them across multiple cameras. It also generated a summary in terms of key frames and textual description of trajectories to a monitoring officer for final analysis and response decision. This level of interpretation was the goal of our research effort, and we believe that it is a significant step forward in the development of intelligent systems that can deal with the complexities of real world scenarios