Boosted local structured HOG-LBP for object localization

By Junge Zhang, Kaiqi Huang, Yinan Yu, Tien-Ping Tan. 2011

170 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation (2013, background): “This hypothesis is grounded, for example, in the wide range of papers that attempt to boost detection accuracy with work along four axes: (1) rich structured models [20, 42]; (2) multiple feature learning [38, 41]; (3) learned histogram-based features [11, 29, 32]; or (4) unsupervised feature learni”
DenseBox: Unifying Landmark Localization with End to End Object Detection (2015, methodology): “First, handcrafted image features such as HOG [5, 45, 44], SIFT [25], and Fisher Vector [4] are extracted at every location and scale of an image.”
DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection (2019, methodology): “After that, a variety of object detection approaches based on handcrafted features and machine learning were proposed one after another and performed well in successive PASCAL VOC object detection challenges [3] [4].”
Feature representation for statistical-learning-based object detection: A review (2015, methodology): “Furthermore, multiple kernel learning (MKL), which can provide independent similarity measurements for different base features, is applied to computing the classifier weights of concatenated vectors [109][110].”
Color attributes for object detection (2012, methodology): “Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity.”
Virtual and Real World Adaptation for Pedestrian Detection (2014, background): “Index Terms—Pedestrian detection, photo-realistic computer animation, data set shift, domain adaptation Ç”
Automated identification of animal species in camera trap images (2013, methodology): “[14] to extract HOG and cLBP, and concatenate responses only after coding them separately.”
Regionlets for Generic Object Detection (2013, methodology): “We do not use any context cues in this paper and leave it as a future work.”