Combining Generative/Discriminative Learning for Automatic Image Annotation and Retrieval

Li, Zhixin; Tang, Zhenjun; Zhao, Weizhong; Li, Zhiqing

doi:10.4236/ijis.2012.23008

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…Since there is no standard human pose depth image library, we builds a data set, including common human actions such as running, jumping, lifting, bending, knee flexion, and interaction. Random forest learning algorithm belongs to supervised learning; the data samples are a known category, and these samples need to be tagged [36][37][38][39]. The tagging method is to divide the human body into 11 parts, and the rest is the background; the approximate position of each part of the human body in the depth image is observed, and then, the position is tagged with the corresponding color.…”

Section: Tagging Body Partsmentioning

confidence: 99%

Human Pose Recognition Based on Depth Image Multifeature Fusion

Wang

Zhou

et al. 2018

Complexity

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The recognition of human pose based on machine vision usually results in a low recognition rate, low robustness, and low operating efficiency. That is mainly caused by the complexity of the background, as well as the diversity of human pose, occlusion, and selfocclusion. To solve this problem, a feature extraction method combining directional gradient of depth feature (DGoD) and local difference of depth feature (LDoD) is proposed in this paper, which uses a novel strategy that incorporates eight neighborhood points around a pixel for mutual comparison to calculate the difference between the pixels. A new data set is then established to train the random forest classifier, and a random forest two-way voting mechanism is adopted to classify the pixels on different parts of the human body depth image. Finally, the gravity center of each part is calculated and a reasonable point is selected as the joint to extract human skeleton. The experimental results show that the robustness and accuracy are significantly improved, associated with a competitive operating efficiency by evaluating our approach with the proposed data set.

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Section: Tagging Body Partsmentioning

confidence: 99%

Human Pose Recognition Based on Depth Image Multifeature Fusion

Wang

Zhou

et al. 2018

Complexity

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“…The computation time is less than LSA. Li et al [19] have designed a hybrid model to overcome the semantic gap in image retrieval and for automatic image annotation. In this framework, continuous probabilistic latent semantic analysis (PLSA) method is used in productive stage of learning to generate visual features of images.…”

Section: Annotation-based Image Retrievalmentioning

confidence: 99%

Image recommendation based on keyword relevance using absorbing Markov chain and image features

Sejal

Rashmi

Venugopal

et al. 2016

Int J Multimed Info Retr

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Image recommendation is an important feature of search engine, as tremendous amount of images are available online. It is necessary to retrieve relevant images to meet the user's requirement. In this paper, we present an algorithm image recommendation with absorbing Markov chain (IRAbMC) to retrieve relevant images for a user's input query. Images are ranked by calculating keyword relevance probability between annotated keywords from log and keywords of user input query. Keyword relevance is computed using absorbing Markov chain. Images are reranked using image visual features. Experimental results show that the IRAbMC algorithm outperforms Markovian semantic indexing (MSI) method with improved relevance score of retrieved ranked images.

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“…With the development of artificial intelligence technology, multimodal fusion is an inevitable trend, and multimodal fusion can make more effective use of the characteristics of the image. In practice, a large amount of labeled data is difficult to obtain, but GAN can solve this problem very well [ 8 , 9 ]. Therefore, the problem of how to combine GAN and noise reduction came into being.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Learning Image Denoising Algorithm Based on Eigenvalue Extraction and the GAN Model

Feng

et al. 2022

Computational Intelligence and Neuroscience

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This paper proposes a self-adjusting generative confrontation network image denoising algorithm. The algorithm combines noise reduction and the adaptive learning GAN model. First, the algorithm uses image features to preprocess the image and extract the effective information of the image. Then, the edge signal is classified according to the threshold value to suppress the problem of “excessive strangulation,” and then the edge signal of the image is extracted to enhance the effective signal in the high-frequency signal. Finally, the algorithm uses an adaptive learning GAN model to further train the image. Each iteration of the generator network is composed of three stages. And then, we get the best value. Through experiments, it can be seen from the data that the article algorithm is compared with the traditional algorithm and the literature algorithm. Under the same conditions, the algorithm can ensure the operating efficiency while having better fidelity, and it can still denoise at the same time. The edge signal of the image is preserved and has a better visual effect.

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Combining Generative/Discriminative Learning for Automatic Image Annotation and Retrieval

Cited by 6 publications

References 28 publications

Human Pose Recognition Based on Depth Image Multifeature Fusion

Human Pose Recognition Based on Depth Image Multifeature Fusion

Image recommendation based on keyword relevance using absorbing Markov chain and image features

An Adaptive Learning Image Denoising Algorithm Based on Eigenvalue Extraction and the GAN Model

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