302,355 research outputs found
Fast Low-rank Representation based Spatial Pyramid Matching for Image Classification
Spatial Pyramid Matching (SPM) and its variants have achieved a lot of
success in image classification. The main difference among them is their
encoding schemes. For example, ScSPM incorporates Sparse Code (SC) instead of
Vector Quantization (VQ) into the framework of SPM. Although the methods
achieve a higher recognition rate than the traditional SPM, they consume more
time to encode the local descriptors extracted from the image. In this paper,
we propose using Low Rank Representation (LRR) to encode the descriptors under
the framework of SPM. Different from SC, LRR considers the group effect among
data points instead of sparsity. Benefiting from this property, the proposed
method (i.e., LrrSPM) can offer a better performance. To further improve the
generalizability and robustness, we reformulate the rank-minimization problem
as a truncated projection problem. Extensive experimental studies show that
LrrSPM is more efficient than its counterparts (e.g., ScSPM) while achieving
competitive recognition rates on nine image data sets.Comment: accepted into knowledge based systems, 201
Recognizing actions from still images
In this paper, we approach the problem of understanding human actions from still images. Our method involves representing the pose with a spatial and orientational histogramming of rectangular regions on a parse probability map. We use LDA to obtain a more compact and discriminative feature representation and binary SVMs for classification. Our results over a new dataset collected for this problem show that by using a rectangle histogramming approach, we can discriminate actions to a great extent. We also show how we can use this approach in an unsupervised setting. To our best knowledge, this is one of the first studies that try to recognize actions within still images
ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction
Effective molecular representation learning is of great importance to
facilitate molecular property prediction, which is a fundamental task for the
drug and material industry. Recent advances in graph neural networks (GNNs)
have shown great promise in applying GNNs for molecular representation
learning. Moreover, a few recent studies have also demonstrated successful
applications of self-supervised learning methods to pre-train the GNNs to
overcome the problem of insufficient labeled molecules. However, existing GNNs
and pre-training strategies usually treat molecules as topological graph data
without fully utilizing the molecular geometry information. Whereas, the
three-dimensional (3D) spatial structure of a molecule, a.k.a molecular
geometry, is one of the most critical factors for determining molecular
physical, chemical, and biological properties. To this end, we propose a novel
Geometry Enhanced Molecular representation learning method (GEM) for Chemical
Representation Learning (ChemRL). At first, we design a geometry-based GNN
architecture that simultaneously models atoms, bonds, and bond angles in a
molecule. To be specific, we devised double graphs for a molecule: The first
one encodes the atom-bond relations; The second one encodes bond-angle
relations. Moreover, on top of the devised GNN architecture, we propose several
novel geometry-level self-supervised learning strategies to learn spatial
knowledge by utilizing the local and global molecular 3D structures. We compare
ChemRL-GEM with various state-of-the-art (SOTA) baselines on different
molecular benchmarks and exhibit that ChemRL-GEM can significantly outperform
all baselines in both regression and classification tasks. For example, the
experimental results show an overall improvement of 8.8% on average compared to
SOTA baselines on the regression tasks, demonstrating the superiority of the
proposed method
SK-Net: Deep Learning on Point Cloud via End-to-end Discovery of Spatial Keypoints
Since the PointNet was proposed, deep learning on point cloud has been the
concentration of intense 3D research. However, existing point-based methods
usually are not adequate to extract the local features and the spatial pattern
of a point cloud for further shape understanding. This paper presents an
end-to-end framework, SK-Net, to jointly optimize the inference of spatial
keypoint with the learning of feature representation of a point cloud for a
specific point cloud task. One key process of SK-Net is the generation of
spatial keypoints (Skeypoints). It is jointly conducted by two proposed
regulating losses and a task objective function without knowledge of Skeypoint
location annotations and proposals. Specifically, our Skeypoints are not
sensitive to the location consistency but are acutely aware of shape. Another
key process of SK-Net is the extraction of the local structure of Skeypoints
(detail feature) and the local spatial pattern of normalized Skeypoints
(pattern feature). This process generates a comprehensive representation,
pattern-detail (PD) feature, which comprises the local detail information of a
point cloud and reveals its spatial pattern through the part district
reconstruction on normalized Skeypoints. Consequently, our network is prompted
to effectively understand the correlation between different regions of a point
cloud and integrate contextual information of the point cloud. In point cloud
tasks, such as classification and segmentation, our proposed method performs
better than or comparable with the state-of-the-art approaches. We also present
an ablation study to demonstrate the advantages of SK-Net
Conceptual spatial representations for indoor mobile robots
We present an approach for creating conceptual representations of human-made indoor environments using mobile
robots. The concepts refer to spatial and functional properties of typical indoor environments. Following ļ¬ndings
in cognitive psychology, our model is composed of layers representing maps at diļ¬erent levels of abstraction. The
complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition.
The system also incorporates a linguistic framework that actively supports the map acquisition process, and which
is used for situated dialogue. Finally, we discuss the capabilities of the integrated system
Pemilihan kerjaya di kalangan pelajar aliran perdagangan sekolah menengah teknik : satu kajian kes
This research is a survey to determine the career chosen of form four student
in commerce streams. The important aspect of the career chosen has been divided
into three, first is information about career, type of career and factor that most
influence students in choosing a career. The study was conducted at Sekolah
Menengah Teknik Kajang, Selangor Darul Ehsan. Thirty six form four students was
chosen by using non-random sampling purpose method as respondent. All
information was gather by using questionnaire. Data collected has been analyzed in
form of frequency, percentage and mean. Results are performed in table and graph.
The finding show that information about career have been improved in students
career chosen and mass media is the main factor influencing students in choosing
their career
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