Semi-supervised learning of object categories from paired local features

Wu, Wen; Yang, Jie

doi:10.1145/1386352.1386386

Cited by 4 publications

(4 citation statements)

References 28 publications

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“…The Mallows distance is then adopted to combine multiple cues from statistics of local matches. This experiment [7] confirm that SSL based approach not only boost classification performance but also improve robustness of the learned category model using only simple local key point features. This paper [1] describes Smart Canvas, an intelligent desk system that allows a user to perform freehand drawing on a desk or similar surface with gestures.…”

Section: Related Worksupporting

confidence: 76%

Learnart : Drawing Environment using Convolutional Neural Networks

2019

ijrte

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Simulating human consciousness and emotions is still the realm of science fiction. The future of neural networks will not exist in tasks to simulate realization. Nowadays, further learning is based on training the machine to recognize the target that might be image or words using datasets. The project is based on convolutional neural networks and weak artificial intelligence. It uses Back Propagation algorithm. It acts as a smart drawing platform for children. Predefined datasets will be framed when it is developed so that users cannot alter it. Users will be the one who draw and checks its efficiency. Suggestion will be displayed if it is right or wrong. The drawings will be shapes and real time objects. The platform gets trained by all these datasets and recognizes the object. The drawing is stored as pixels pattern and checks with the previous data and finds how much percentage does the current drawing is matching with the previous drawings. By the help of this application, children can develop basic single object drawing

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Section: Related Worksupporting

confidence: 76%

Learnart : Drawing Environment using Convolutional Neural Networks

2019

ijrte

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“…Local feature sets of images are matched with each other by the method of [9]. It is based on the criterion proposed by Lowe [10], which is defined as a threshold on the ratio of distance from the closest neighbor to that of the second-closest neighbor.…”

Section: Building Graph Based On Local Matchesmentioning

confidence: 99%

“…We demonstrate the promising results on two multi-label image datasets: MSRC and Corel subsets. Wu et al [9] also proposed a graph based semi-supervised learning framework using the local feature matching. However, they only considered single-label cases and the graph is constructed with image pairs other than local features.…”

Section: Introductionmentioning

confidence: 99%

Local-driven semi-supervised learning with multi-label

Yan

Mei

et al. 2009

2009 IEEE International Conference on Multimedia and Expo

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In this paper, we present a local-driven semi-supervised learning framework to propagate the labels of the training data (with multi-label) to the unlabeled data. Instead of using each datum as a vertex of graph, we encode each extracted local feature descriptor as a vertex, and then the labels for each vertex from the training data are derived based on the context among different training data, finally the decomposed labels on each vertex are further propagated to the unlabeled vertices based on the similarities measured according to the features extracted at each local regions. With the learnt local descriptor graph we can predict the semantic labels for not only the test local features but also the test images. The experiments on multi-label image annotation demonstrate the encouraging results from our proposed framework of semi-supervised learning.

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“…In order to reduce human labor, learning algorithms such as online learning [1] [3] and semi-supervised learning algorithms [9] are proposed to use in object detection works. Online learning algorithms try to incrementally utilize the incoming unlabeled samples based on an automatic labeler, while semi-supervised learning algorithms are also well known for leveraging a large amount of unlabeled data as well as a small amount of labeled data to improve classification performance.…”

Section: Introductionmentioning

confidence: 99%

Shape-based web image clustering for unsupervised object detection?

Zheng¹,

Wang

Chen

2011

2011 IEEE International Conference on Multimedia and Expo

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Automatic object detection for an arbitrary class is an important but very challenging problem, due to the countless kinds of objects in the world and the large amount of labeling work for each object. In this work, we target at solving the problem of automatic object detection for an arbitrary class without the laborious human effort. Motivated by the explosive growth of Web images and the phenomenal success of search techniques, we develop an unsupervised object detection framework by automatically training the object detector on the top returns of certain image search engine queried by the name of the object class. In order to automatically isolate the objects from the Web images for training, only clipart images with simple background are used, which preserve most of the shape information of the objects. A two-stage shape-based clustering algorithm is proposed to mine typical shapes of the object, in which the inner-class variance of object shapes is considered and undesired images are filtered out. In order to reduce the gap between clipart images and realworld images, we introduce an efficient algorithm to synthesize the real-world images from clipart images, and only shape feature is used in the detector training part. Finally, the synthetic images could be used to train object detectors by an off-the-shelf discriminative algorithm, e.g., boosting or SVM. Extensive experiments show the effectiveness of the proposed framework on objects with simple and representative shapes, and the proposed framework could be considered as a good beginning of solving this challenging problem.

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Semi-supervised learning of object categories from paired local features

Cited by 4 publications

References 28 publications

Learnart : Drawing Environment using Convolutional Neural Networks

Learnart : Drawing Environment using Convolutional Neural Networks

Local-driven semi-supervised learning with multi-label

Shape-based web image clustering for unsupervised object detection?

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