| 1. |
- Antanas, Laura, et al.
(författare)
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A relational kernel-based approach to scene classification
- 2013
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Ingår i: Proceedings of IEEE Workshop on Applications of Computer Vision. - : IEEE. - 9781467350532 - 9781467350549 ; , s. 133-139
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Konferensbidrag (refereegranskat)abstract
- Real-world scenes involve many objects that interact with each other in complex semantic patterns. For example, a bar scene can be naturally described as having a variable number of chairs of similar size, close to each other and aligned horizontally. This high-level interpretation of a scene relies on semantically meaningful entities and is most generally described using relational representations or (hyper-) graphs. Popular in early work on syntactic and structural pattern recognition, relational representations are rarely used in computer vision due to their pure symbolic nature. Yet, today recent successes in combining them with statistical learning principles motivates us to reinvestigate their use. In this paper we show that relational techniques can also improve scene classification. More specifically, we employ a new relational language for learning with kernels, called kLog. With this language we define higher-order spatial relations among semantic objects. When applied to a particular image, they characterize a particular object arrangement and provide discriminative cues for the scene category. The kernel allows us to tractably learn from such complex features. Thus, our contribution is a principled and interpretable approach to learn from symbolic relations how to classify scenes in a statistical framework. We obtain results comparable to state-of-the-art methods on 15 Scenes and a subset of the MIT indoor dataset.
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| 2. |
- Antanas, Laura, et al.
(författare)
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Relational Affordance Learning for Task-Dependent Robot Grasping
- 2018
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Ingår i: Inductive Logic Programming. - Cham : Springer International Publishing. - 9783319780900 - 9783319780894 ; , s. 1-15
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Konferensbidrag (refereegranskat)abstract
- Robot grasping depends on the specific manipulation scenario: the object, its properties, task and grasp constraints. Object-task affordances facilitate semantic reasoning about pre-grasp configurations with respect to the intended tasks, favoring good grasps. We employ probabilistic rule learning to recover such object-task affordances for task-dependent grasping from realistic video data.
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| 3. |
- Antanas, Laura, et al.
(författare)
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Relational Kernel-Based Grasping with Numerical Features
- 2016
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Ingår i: Inductive Logic Programming. - Cham : Springer. - 9783319405650 - 9783319405667 ; , s. 1-14
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Konferensbidrag (refereegranskat)abstract
- Object grasping is a key task in robot manipulation. Performing a grasp largely depends on the object properties and grasp constraints. This paper proposes a new statistical relational learning approach to recognize graspable points in object point clouds. We characterize each point with numerical shape features and represent each cloud as a (hyper-) graph by considering qualitative spatial relations between neighboring points. Further, we use kernels on graphs to exploit extended contextual shape information and compute discriminative features which show improvement upon local shape features. Our work for robot grasping highlights the importance of moving towards integrating relational representations with low-level descriptors for robot vision. We evaluate our relational kernel-based approach on a realistic dataset with 8 objects.
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| 4. |
- Antanas, Laura, et al.
(författare)
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Semantic and geometric reasoning for robotic grasping : a probabilistic logic approach
- 2019
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Ingår i: Autonomous Robots. - : Springer. - 0929-5593 .- 1573-7527. ; 43:6, s. 1393-1418
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Tidskriftsartikel (refereegranskat)abstract
- While any grasp must satisfy the grasping stability criteria, good grasps depend on the specific manipulation scenario: the object, its properties and functionalities, as well as the task and grasp constraints. We propose a probabilistic logic approach for robot grasping, which improves grasping capabilities by leveraging semantic object parts. It provides the robot with semantic reasoning skills about the most likely object part to be grasped, given the task constraints and object properties, while also dealing with the uncertainty of visual perception and grasp planning. The probabilistic logic framework is task-dependent. It semantically reasons about pre-grasp configurations with respect to the intended task and employs object-task affordances and object/task ontologies to encode rules that generalize over similar object parts and object/task categories. The use of probabilistic logic for task-dependent grasping contrasts with current approaches that usually learn direct mappings from visual perceptions to task-dependent grasping points. The logic-based module receives data from a low-level module that extracts semantic objects parts, and sends information to the low-level grasp planner. These three modules define our probabilistic logic framework, which is able to perform robotic grasping in realistic kitchen-related scenarios.
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| 5. |
- Antanas, Laura, et al.
(författare)
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There are plenty of places like home : Using relational representations in hierarchies for distance-based image understanding
- 2014
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Ingår i: Neurocomputing. - : Elsevier. - 0925-2312 .- 1872-8286. ; 123, s. 75-85
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Tidskriftsartikel (refereegranskat)abstract
- Understanding images in terms of logical and hierarchical structures is crucial for many semantic tasks, including image retrieval, scene understanding and robotic vision. This paper combines robust feature extraction, qualitative spatial relations, relational instance-based learning and compositional hierarchies in one framework. For each layer in the hierarchy, qualitative spatial structures in images are detected, classified and then employed one layer up the hierarchy to obtain higher-level semantic structures. We apply a four-layer hierarchy to street view images and subsequently detect corners, windows, doors, and individual houses.
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| 6. |
- Babaki, Behrouz, et al.
(författare)
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Constraint-Based Querying for Bayesian Network Exploration
- 2015
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Ingår i: Advances in Intelligent Data Analysis XIV. - Cham : Springer International Publishing. - 9783319244648 - 9783319244655 ; , s. 13-24
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Konferensbidrag (refereegranskat)abstract
- Understanding the knowledge that resides in a Bayesian network can be hard, certainly when a large network is to be used for the first time, or when the network is complex or has just been updated. Tools to assist users in the analysis of Bayesian networks can help. In this paper, we introduce a novel general framework and tool for answering exploratory queries over Bayesian networks. The framework is inspired by queries from the constraint-based mining literature designed for the exploratory analysis of data. Adapted to Bayesian networks, these queries specify a set of constraints on explanations of interest, where an explanation is an assignment to a subset of variables in a network. Characteristic for the methodology is that it searches over different subsets of the explanations, corresponding to different marginalizations. A general purpose framework, based on principles of constraint programming, data mining and knowledge compilation, is used to answer all possible queries. This CP4BN framework employs a rich set of constraints and is able to emulate a range of existing queries from both the Bayesian network and the constraint-based data mining literature.
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| 7. |
- Babaki, Behrouz, et al.
(författare)
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Stochastic Constraint Programming with And-Or Branch-and-Bound
- 2017
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Ingår i: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. - California : AAAI Press. - 9780999241103 ; , s. 539-545
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Konferensbidrag (refereegranskat)abstract
- Complex multi-stage decision making problems often involve uncertainty, for example, regarding demand or processing times. Stochastic constraint programming was proposed as a way to formulate and solve such decision problems, involving arbitrary constraints over both decision and random variables. What stochastic constraint programming currently lacks is support for the use of factorized probabilistic models that are popular in the graphical model community. We show how a state-of-the-art probabilistic inference engine can be integrated into standard constraint solvers. The resulting approach searches over the And-Or search tree directly, and we investigate tight bounds on the expected utility objective. This significantly improves search efficiency and outperforms scenario-based methods that ground out the possible worlds.
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| 8. |
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| 9. |
- Bessiere, Christian, et al.
(författare)
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The Inductive Constraint Programming Loop
- 2017
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Ingår i: IEEE Intelligent Systems. - New York : Institute of Electrical and Electronics Engineers (IEEE). - 1541-1672 .- 1941-1294. ; 32:5, s. 44-52
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Tidskriftsartikel (refereegranskat)abstract
- Constraint programming is used for a variety of real-world optimisation problems, such as planning, scheduling and resource allocation problems. At the same time, one continuously gathers vast amounts of data about these problems. Current constraint programming software does not exploit such data to update schedules, resources and plans. We propose a new framework, that we call the Inductive Constraint Programming loop. In this approach data is gathered and analyzed systematically, in order to dynamically revise and adapt constraints and optimization criteria. Inductive Constraint Programming aims at bridging the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other hand.
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| 10. |
- Bessiere, Christian, et al.
(författare)
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The Inductive Constraint Programming Loop
- 2016
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Ingår i: Data Mining and Constraint Programming. - Cham : Springer International Publishing. - 9783319501369 - 9783319501376 ; , s. 303-309
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Bokkapitel (refereegranskat)abstract
- Constraint programming is used for a variety of real-world optimisa-tion problems, such as planning, scheduling and resource allocation prob-lems. At the same time, one continuously gathers vast amounts of dataabout these problems. Current constraint programming software does notexploit such data to update schedules, resources and plans. We propose anew framework, that we call theInductive Constraint Programming loop.In this approach data is gathered and analyzed systematically, in order todynamically revise and adapt constraints and optimization criteria. In-ductive Constraint Programming aims at bridging the gap between theareas of data mining and machine learning on the one hand, and constraintprogramming on the other hand.
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