10,376 research outputs found
Enhanced spatial pyramid matching using log-polar-based image subdivision and representation
This paper presents a new model for capturing spatial information for object categorization with bag-of-words (BOW). BOW models have recently become popular for the task of object recognition, owing to their good performance and simplicity. Much work has been proposed over the years to improve the BOW model, where the Spatial Pyramid Matching (SPM) technique is the most notable. We propose a new method to exploit spatial relationships between image features, based on binned log-polar grids. Our model works by partitioning the image into grids of different scales and orientations and computing histogram of local features within each grid. Experimental results show that our approach improves the results on three diverse datasets over the SPM technique
Improving Bag-of-Words model with spatial information
Bag-of-Words (BOW) models have recently become popular for the task of object recognition, owing to their good performance and simplicity. Much work has been proposed over the years to improve the BOW model, where the Spatial Pyramid Matching technique is the most notable. In this work, we propose three novel techniques to capture more re_ned spatial information between image features than that provided by the Spatial Pyramids. Our techniques demonstrate a performance gain over the Spatial Pyramid representation of the BOW model
DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection
Although YOLOv2 approach is extremely fast on object detection; its backbone
network has the low ability on feature extraction and fails to make full use of
multi-scale local region features, which restricts the improvement of object
detection accuracy. Therefore, this paper proposed a DC-SPP-YOLO (Dense
Connection and Spatial Pyramid Pooling Based YOLO) approach for ameliorating
the object detection accuracy of YOLOv2. Specifically, the dense connection of
convolution layers is employed in the backbone network of YOLOv2 to strengthen
the feature extraction and alleviate the vanishing-gradient problem. Moreover,
an improved spatial pyramid pooling is introduced to pool and concatenate the
multi-scale local region features, so that the network can learn the object
features more comprehensively. The DC-SPP-YOLO model is established and trained
based on a new loss function composed of mean square error and cross entropy,
and the object detection is realized. Experiments demonstrate that the mAP
(mean Average Precision) of DC-SPP-YOLO proposed on PASCAL VOC datasets and
UA-DETRAC datasets is higher than that of YOLOv2; the object detection accuracy
of DC-SPP-YOLO is superior to YOLOv2 by strengthening feature extraction and
using the multi-scale local region features.Comment: 23 pages, 9 figures, 9 table
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