Latent topics-based relevance feedback for video retrieval

Fernández-Beltrán, Rubén; Pla, Filiberto

doi:10.1016/j.patcog.2015.09.007

Cited by 30 publications

(12 citation statements)

References 35 publications

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“…Content-based retrieval refers to retrieval according to the semantic features or audio-visual features of media objects [6][7][8]. Semantic features refer to the content information of video segments, while audio-visual features refer to some physical features that can be directly obtained from sounds and images, such as colors, textures, and shapes in images, motions of objects and lenses in videos, and tonal loudness and timbre in sounds [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

News Video Classification Model Based on ResNet-2 and Transfer Learning

Gao

2021

Security and Communication Networks

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A large amount of useful information is included in the news video, and how to classify the news video information has become an important research topic in the field of multimedia technology. News videos are enormously informative, and employing manual classification methods is too time-consuming and vulnerable to subjective judgment. Therefore, developing an automated news video analysis and retrieval method becomes one of the most important research contents in the current multimedia information system. Therefore, this paper proposes a news video classification model based on ResNet-2 and transfer learning. First, a model-based transfer method was adopted to transfer the commonality knowledge of the pretrained model of the Inception-ResNet-v2 network on ImageNet, and a news video classification model was constructed. Then, a momentum update rule is introduced on the basis of the Adam algorithm, and an improved gradient descent method is proposed in order to obtain an optimal solution of the local minima of the function in the learning process. The experimental results show that the improved Adam algorithm can iteratively update the network weights through the adaptive learning rate to reach the fastest convergence. Compared with other convolutional neural network models, the modified Inception-ResNet-v2 network model achieves 91.47% classification accuracy for common news video datasets.

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Section: Introductionmentioning

confidence: 99%

News Video Classification Model Based on ResNet-2 and Transfer Learning

Gao

2021

Security and Communication Networks

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“…To bridge this gap relevance feedback (RF) is introduced with this forceful image representation by fusion of distinctive local and global features from the image. The RF method adds user feedback to enhance retrieval performance and provides related images [46][47][48][49][50]. Hence the RF is integrated with the proposed system, and it performs an iterative process.…”

Section: Definite Boundary Representationmentioning

confidence: 99%

Enhancing Scalability of Image Retrieval Using Visual Fusion of Feature Descriptors

Balammal@Geetha¹,

Muthukkumar²,

Seenivasagam³

2022

Intelligent Automation &Amp; Soft Computing

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Content-Based Image Retrieval (CBIR) is an approach of retrieving similar images from a large image database. Recently CBIR poses new challenges in semantic categorization of the images. Different feature extraction technique have been proposed to overcome the semantic breach problems, however these methods suffer from several shortcomings. This paper contributes an image retrieval system to extract the local features based on the fusion of scale-invariant feature transform (SIFT) and KAZE. The strength of local feature descriptor SIFT complements global feature descriptor KAZE. SIFT concentrates on the complete region of an image using high fine points of features and KAZE ponders on details of a boundary. The fusion of local feature descriptor and global feature descriptor boost the retrieval of images having diverse semantic classification and also helps in achieving the better results in large scale retrieval. To enhance the scalability of image retrieval bag of visual words (BoVW) is mainly used. The fusion of local and global feature representations are selected for image retrieval for the reason that SIFT effectively captures shape and texture and robust towards the change in scale and rotation, while KAZE have strong response towards boundary and changes in illumination. Experiments conducted on two image collections, namely, Caltech-256 and Corel 10k demonstrate the proposed scheme appreciably enhanced the performance of the CBIR compared to state-of-the-art image retrieval techniques.

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“…Sentiment classification used the machine learning technique on TensorFlow [35]. TensorFlow is software used for sentiment training in the LSTM method [36].…”

Section: Aspect Based Sentiment Classification Using Word Embedding +mentioning

confidence: 99%

Sentiment Analysis of Hotel Aspect Using Probabilistic Latent Semantic Analysis, Word Embedding and LSTM

Khotimah¹,

Sarno²

2019

IJIES

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In the industrial era 5.0, product reviews are necessary for the sustainability of a company. Product reviews are a User Generated Content (UGC) feature which describes customer satisfaction. The researcher used five hotel aspects including location, meal, service, comfort, and cleanliness to measure customer satisfaction. Each product review was preprocessed into a term list document. In this context, we proposed the Probabilistic Latent Semantic Analysis (PLSA) method to produce a hidden topic. Semantic Similarity was used to classify topics into five hotel aspects. The Term Frequency-Inverse Corpus Frequency (TF-ICF) method was used for weighting each term list, which had been expanded from each cluster in the document. The researcher used Word embedding to obtain vector values in the deep learning method from Long Short-Term Memory (LSTM) for sentiment classification. The result showed that the combination of the PLSA + TF ICF 100% + Semantic Similarity method was superior are 0.840 in the fifth categorization of the hotel aspects; the Word Embedding + LSTM method outperformed the sentiment classification at value 0.946; the service aspect received positive sentiment value higher are 45.545 than the other aspects; the comfort aspect received negative sentiment value higher are 12.871 than the other aspects. Other results also showed that sentiment was affected by the aspects.

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Latent topics-based relevance feedback for video retrieval

Cited by 30 publications

References 35 publications

News Video Classification Model Based on ResNet-2 and Transfer Learning

News Video Classification Model Based on ResNet-2 and Transfer Learning

Enhancing Scalability of Image Retrieval Using Visual Fusion of Feature Descriptors

Sentiment Analysis of Hotel Aspect Using Probabilistic Latent Semantic Analysis, Word Embedding and LSTM

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