Projective nonnegative matrix factorization for social image retrieval

Liu, Qiuli; Li, Zechao

doi:10.1016/j.neucom.2014.09.094

Cited by 12 publications

(4 citation statements)

References 38 publications

(54 reference statements)

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“…In the recovering step, the time complexity is O(mkn), which is approximate to O(m). Therefore, the overall time complexity of Algorithm 1 is approximate to O(t in F k m + 2m 2 + +2n 2 + m).…”

Section: Time Complexity Of Cg-cnmfmentioning

confidence: 99%

Cograph Regularized Collective Nonnegative Matrix Factorization for Multilabel Image Annotation

Zhang

et al. 2019

IEEE Access

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Automatic image annotation is an effective and straightforward way to facilitate many applications in computer vision. However, manually annotating images is a computation-expensive and labor-intensive task. To address these problems, this paper proposes a novel approach by using a cograph regularized collective nonnegative matrix factorization method to annotate images, which is referred to as CG-CNMF; CG-CNMF maximizes the annotation consistency for each image and minimizes the semantic gap for good annotation performance. To reduce the computation cost, this method formulates the annotation problem as a recommending issue and uses nonnegative matrix factorization (NMF) to recover the image-tolabel relation for the testing images. Moreover, to find the most similar latent image features and latent label features during the matrix factorization, it exploits the image-to-image relation and label-to-label relation by utilizing the visual content information of images and the semantic cooccurrence information of labels, respectively. To reduce the semantic gap between the image visual content and semantic concepts, both the semantic features and convolutional neural networks (CNNs)-based visual features are considered. Moreover, to address the label-imbalance and incomplete-label problems, the visual-based label cooccurrence information is also considered. In this way, visually similar images are highly correlated with the true semantics of the test images. The experimental results for three multilabel image datasets demonstrate the effectiveness and the efficiency of the proposed method.

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Section: Time Complexity Of Cg-cnmfmentioning

confidence: 99%

Cograph Regularized Collective Nonnegative Matrix Factorization for Multilabel Image Annotation

Zhang

et al. 2019

IEEE Access

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“…(2) In order to provide the more flexible method to select the number of visual-topics, DAC_mmst is the first joint model of image annotation and classification based on non-parametric HDP, which models each image as a Dirichlet process for topics discovery. (3) We significantly boost the classification accuracy by considering the deeply non-visual topics.…”

Section: Related Workmentioning

confidence: 99%

“…Image annotation task aims to develop techniques to reliably describe the different objects depicted in the images by borrowing some annotation terms [2]. Matrix factorization [3], multi-label learning [4,5] and probabilistic topic models [6,7,8,9,10,11] have been developed. Given an image, image classification tells people what is the theme of the image according to its visual content from a high-level semantic meaning perspective.…”

Section: Introductionmentioning

confidence: 99%

Deep Image Annotation and Classification by Fusing Multi-Modal Semantic Topics

2018

KSII TIIS

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Due to the semantic gap problem across different modalities, automatically retrieval from multimedia information still faces a main challenge. It is desirable to provide an effective joint model to bridge the gap and organize the relationships between them. In this work, we develop a deep image annotation and classification by fusing multi-modal semantic topics (DAC_mmst) model, which has the capacity for finding visual and non-visual topics by jointly modeling the image and loosely related text for deep image annotation while simultaneously learning and predicting the class label. More specifically, DAC_mmst depends on a non-parametric Bayesian model for estimating the best number of visual topics that can perfectly explain the image. To evaluate the effectiveness of our proposed algorithm, we collect a real-world dataset to conduct various experiments. The experimental results show our proposed DAC_mmst performs favorably in perplexity, image annotation and classification accuracy, comparing to several state-of-the-art methods.

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“…TM is a fundamental problem of pattern recognition and has a wide range of applications in the field of image processing and computer vision, such as image recognition [2][3][4][5], remote sensing [6,7], social media analytics [8,9], medical image processing [10][11][12], biometric recognition [13][14][15], etc. In image analysis, matching technologies play an important role in image understanding and retrieval [16].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Rao-NM Algorithm for Image Template Matching

Liu

Wang

et al. 2021

Entropy

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This paper proposes a hybrid Rao-Nelder–Mead (Rao-NM) algorithm for image template matching is proposed. The developed algorithm incorporates the Rao-1 algorithm and NM algorithm serially. Thus, the powerful global search capability of the Rao-1 algorithm and local search capability of NM algorithm is fully exploited. It can quickly and accurately search for the high-quality optimal solution on the basis of ensuring global convergence. The computing time is highly reduced, while the matching accuracy is significantly improved. Four commonly applied optimization problems and three image datasets are employed to assess the performance of the proposed method. Meanwhile, three commonly used algorithms, including generic Rao-1 algorithm, particle swarm optimization (PSO), genetic algorithm (GA), are considered as benchmarking algorithms. The experiment results demonstrate that the proposed method is effective and efficient in solving image matching problems.

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Projective nonnegative matrix factorization for social image retrieval

Cited by 12 publications

References 38 publications

Cograph Regularized Collective Nonnegative Matrix Factorization for Multilabel Image Annotation

Cograph Regularized Collective Nonnegative Matrix Factorization for Multilabel Image Annotation

Deep Image Annotation and Classification by Fusing Multi-Modal Semantic Topics

A Hybrid Rao-NM Algorithm for Image Template Matching

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