In this paper, we develop a novel method of 3d object classification based on a Two-dimensional Hidden Markov model (2d HMM). Hidden Markov models are a widely used methodology for sequential data modeling, of growing...
详细信息
ISBN:
(纸本)9783037858646
In this paper, we develop a novel method of 3d object classification based on a Two-dimensional Hidden Markov model (2d HMM). Hidden Markov models are a widely used methodology for sequential data modeling, of growing importance in the last years. In the proposed approach, each object is decomposed by a spiderweb model and a shape function d2 is computed for each bin. These feature vectors are then arranged in a sequential fashion to compose a sequence vector, which is used to train HMMs. In 2d HMM, we assume that feature vectors are statistically dependent on an underlying state process which has transition probabilities conditioning the states of two neighboring bins. Thus the dependency of two dimensions is reflected simultaneously. To classify an object, the maximized posteriori probability is calculated by a given model and the observed sequence of an unknown object. Comparing with 1d HMM, the 2d HMM gets more information from the neighboring bins. Analysis and experimental results show that the proposed approach performs better than existing ones in database.
3d model classification is a critical process of building information modeling (BIM). A deep learning approach was proposed to classify 3dmodels in BIM environments. A ray-based feature extraction algorithm was used ...
详细信息
3d model classification is a critical process of building information modeling (BIM). A deep learning approach was proposed to classify 3dmodels in BIM environments. A ray-based feature extraction algorithm was used to extract the features of 3dmodels and form a features matrix. The deep belief network (dBN) constructed by restricted Boltzmann machines applies a features matrix and classifies the models, adopting the effective training process. The process of training dBN is layer by layer. Experiments were taken from a public 3dmodel library of a PSB modeldatabase. The results show that compared with several commonly usedclassification methods, the method proposed in this paper achieved good results in the 3d model classification for efficient BIM.
This paper presents an adaptive-weighted Hidden Markov models (AWHMM) method for classification of 3dmodels into a set of pre-determinatedmodel classes. Two new features are proposed to capture model surface orienta...
详细信息
ISBN:
(纸本)1846000025
This paper presents an adaptive-weighted Hidden Markov models (AWHMM) method for classification of 3dmodels into a set of pre-determinatedmodel classes. Two new features are proposed to capture model surface orientation information. In the method, each model class is represented by four HMMs corresponding to four type features. during the training process, for each type of feature, the feature statistics of each model class and the spatial dynamics are learned by an HMM. during the classification process, characteristics of the test model are analyzed by the HMMs corresponding to each model class. The likelihood scores provided by the HMMs are calculated, and the highest weighted sum score provides the class identification of the test model. Furthermore, with unsupervised learning, each HMM and type weight are adapted with test models, which results in better modeling over time. Based on experiments, the proposed algorithm achieves much better performance than a baseline method and better performance than HMMs using only two features with fixed weights method.
We present a new framework for 3dmodel retrieval based on the assumption that models belonging to the same shape class share the same salient features. The main issue is learning these features. We propose an algorit...
详细信息
ISBN:
(纸本)9781595939128
We present a new framework for 3dmodel retrieval based on the assumption that models belonging to the same shape class share the same salient features. The main issue is learning these features. We propose an algorithm for computing these features and their corresponding saliency value. At the learning stage, a large set of features are extracted from every model and a boosting algorithm is applied to learn the classification function in the feature space. AdaBoost learns a classifier that relies on a small subset of the features with the mean of weak classifiers. Moreover it assigns weights to the selected features, that we interpret as a measure of the feature saliency within the class, providing an efficient way for feature selection and combination. Our experiments using the LightField (LFd) descriptors and the Princeton Shape Benchmark show significant improvement in the retrieval performance and computation efficiency. We show also that the proposed framework can be applied to the problem of best view selection.
Advances in automateddata collection tools in design and manufacturing have far exceeded our capacity to analyze this data for novel information. Techniques of data mining and knowledge discovery in large databases p...
详细信息
ISBN:
(纸本)081945558X
Advances in automateddata collection tools in design and manufacturing have far exceeded our capacity to analyze this data for novel information. Techniques of data mining and knowledge discovery in large databases promise computationally efficient and accurate means to analyze such data for patterns and similar structures. In this paper, we present a unique data mining approach for finding similarities in classes of 3dmodels, using discovery of association rules. PCA is first performed on the 3dmodel to transform it along first principal axis. Transformed3dmodel is then sliced and segmented along multiple principal axes, such that each slice can be interpreted as a transaction in a transaction database. Association-rule discovery is performed on this transaction space for multiple models and common association rules among those transactions are stored as a representative of a class of models. We have evaluated the performance of association rules for efficient representation of classes of shape models. The method is time and space efficient, besides presenting a novel paradigm for searching content dependencies in a database of 3dmodels.
暂无评论