Journal Article10.1109/TSMCB.2011.2172605
Mining Visual Collocation Patterns via Self-Supervised Subspace Learning
Junsong Yuan,Ying Wu +1 more
- 01 Apr 2012
- Vol. 42, Iss: 2, pp 334-346
TL;DR: The novelty of this work lies in a principled solution to the discovery of visual collocation patterns based on frequent itemset mining and a self-supervised subspace learning method to refine the visual codebook by feeding back discovered patterns via sub space learning.
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Abstract: Traditional text data mining techniques are not directly applicable to image data which contain spatial information and are characterized by high-dimensional visual features. It is not a trivial task to discover meaningful visual patterns from images because the content variations and spatial dependence in visual data greatly challenge most existing data mining methods. This paper presents a novel approach to coping with these difficulties for mining visual collocation patterns. Specifically, the novelty of this work lies in the following new contributions: 1) a principled solution to the discovery of visual collocation patterns based on frequent itemset mining and 2) a self-supervised subspace learning method to refine the visual codebook by feeding back discovered patterns via subspace learning. The experimental results show that our method can discover semantically meaningful patterns efficiently and effectively.
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Citations
Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey
TL;DR: According to the inherent defects of vision, this paper summarizes the research achievements of tactile feedback in the fields of target recognition and robot grasping and finds that the combination of vision and tactile feedback can improve the success rate and robustness of robot grasping.
Visual pattern discovery in image and video data: a brief survey
TL;DR: A review of the major progress in visual pattern discovery is provided, categorizing the existing methods into two groups: bottom‐up pattern discovery and top‐down pattern modeling.
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Topical Video Object Discovery from Key Frames by Modeling Word Co-occurrence Prior
Gangqiang Zhao,Junsong Yuan,Gang Hua +2 more
- 23 Jun 2013
TL;DR: A topic model is proposed that incorporates a word co-occurrence prior for efficient discovery of topical video objects from a set of key frames that can discover different types of topical objects despite variations in scale, view-point, color and lighting changes, or even partial occlusions.
Context-Aware Discovery of Visual Co-Occurrence Patterns
TL;DR: A novel self-learning procedure is proposed to utilize the discovered spatial or feature patterns to gradually refine the clustering result, and is guaranteed to converge and experiments on real images validate the effectiveness of the method.
19
Combining Feature Context and Spatial Context for Image Pattern Discovery
Hongxing Wang,Junsong Yuan,Yap-Peng Tan +2 more
- 11 Dec 2011
TL;DR: This work forms the problem as a regularized k-means clustering, and proposes an iterative bottom-up/top-down self-learning procedure to gradually refine the result until it converges, which can better handle the ambiguities of visual primitives, by leveraging these co-occurrences.
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