A Review on Correlation Maximized Similarity Measurement in Cross Media Retrieval Method

2018 ◽  
Vol 6 (3) ◽  
pp. 214-218
Author(s):  
Monelli Ayyavaraiah ◽  
Keyword(s):  
Similarity Measurement ◽  
Retrieval Method ◽  
Cross Media

Cross-Media Retrieval Method Based on Content Correlations

2009 ◽  
Vol 31 (5) ◽  
pp. 820-826 ◽  
Author(s):  
Hong ZHANG ◽  
Fei WU ◽  
Yue-Ting ZHUANG ◽  
Jian-Xun CHEN
Keyword(s):  
Retrieval Method ◽  
Cross Media

scholarly journals Design and Implementation of a Student Archive Retrieval Method Based on Image Processing

2020 ◽  
Vol 34 (4) ◽  
pp. 487-494
Author(s):  
Lei An ◽  
Aihua Li
Keyword(s):  
Image Processing ◽  
Information Management ◽  
Image Feature ◽  
Similarity Measurement ◽  
Function Learning ◽  
Retrieval Method ◽  
Design And Implementation ◽  
Network Modules ◽  
Novel Method ◽  
Effectiveness And Efficiency

Compared with traditional manual archive organization and review, the student archive management system can manage massive student archives in a refined, regular, and scientific manner. The effectiveness and efficiency of the retrieval method directly bears on the utilization effect of student archives. Based on image processing, this paper puts forward a novel method for student archive retrieval, which greatly improves the classification, recognition, and information management of images in student archives during the retrieval. Firstly, a framework of student archive retrieval was introduced based on image processing. Next, a deep convolutional neural network (DCNN) was constructed for hash learning, and the functions of the three network modules were detailed, including image feature extraction, hash function learning, and similarity measurement. Finally, several indices were selected to evaluate the retrieval effect of student archives. The proposed method was proved effective and feasible through contrastive experiments. The research results provide a theoretical reference for the application of our method in other fields of image retrieval.

Cross-Media Retrieval Based on Query Modality and Semi-Supervised Regularization

2017 ◽  
Vol 21 (7) ◽  
pp. 1211-1220
Author(s):  
Yihe Liu ◽  
◽  
Huaxiang Zhang ◽  
Li Liu ◽  
Lili Meng ◽  
...  
Keyword(s):  
Semantic Information ◽  
Structural Information ◽  
Unlabeled Data ◽  
Experimental Results ◽  
Retrieval Method ◽  
Cross Media ◽  
Feature Spaces ◽  
Media Objects ◽  
The Cross ◽  
Original Feature

Existing cross-media retrieval methods usually learn one same latent subspace for different retrieval tasks, which can only achieve a suboptimal retrieval. In this paper, we propose a novel cross-media retrieval method based on Query Modality and Semi-supervised Regularization (QMSR). Taking the cross-media retrieval between images and texts for example, QMSR learns two couples of mappings for different retrieval tasks (i.e. using images to search texts (Im2Te) or using texts to search images (Te2Im)) instead of learning one couple of mappings. QMSR learns two couples of projections by optimizing the correlation between images and texts and the semantic information of query modality (image or text), and integrates together the semi-supervised regularization, the structural information among both labeled and unlabeled data of query modality to transform different media objects from original feature spaces into two different isomorphic subspaces (Im2Te common subspace and Te2Im common subspace). Experimental results show the effectiveness of the proposed method.

scholarly journals A Cross-Media Retrieval Method Based on Semisupervised Learning and Alternate Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Junzheng Li ◽  
Wei Zhu ◽  
Yanchun Yang ◽  
Xiyuan Zheng
Keyword(s):  
Big Data ◽  
Optimization Method ◽  
Semisupervised Learning ◽  
Multimedia Data ◽  
Internet Technology ◽  
Retrieval Method ◽  
Cross Media ◽  
Media Learning ◽  
Public Datasets ◽  
Alternate Optimization

With the continuous advancement in Internet technology, we are gradually stepping into an era of big data where a large amount of multimedia data is produced every day at any given time. In order to properly utilize these data, the research on big data is also constantly evolving. Cross-media retrieval is a prime example, aiming at retrieving various forms of data, for example, text, image, audio, video, and other forms. The most difficult task for cross-media retrieval lies in the potential correlation between different modalities data and how to overcome the semantic gap. This paper proposes a cross-media retrieval method based on semisupervised learning and alternate optimization (SMDCR) to overcome the abovementioned difficulties, thereby improving the retrieval accuracy. The main advantage of this method is to make full use of the degree of correlation between the semantic information of the labeled data and unlabeled data. Simultaneously, we combine the linear regression term, correlation analysis term, and feature selection term into a joint cross-media learning framework. Furthermore, the projection matrices are trained with the alternate optimization method. Finally, experimental results on two public datasets demonstrate the effectiveness of the proposed method.

Cross-Media Retrieval Method Based on Space Mapping

2013 ◽  
Vol 756-759 ◽  
pp. 1898-1902
Author(s):  
Xin Xu ◽  
Su Mei Xi
Keyword(s):  
Recall Performance ◽  
Space Mapping ◽  
Multimedia Data ◽  
Retrieval Method ◽  
New Approach ◽  
Cross Media ◽  
Audio Data ◽  
Auditory Features

This paper puts forward a novel cross-media retrieval approach, which can process multimedia data of different modalities and measure cross-media similarity, such as image-audio similarity. Both image and audio data are selected for experiments and comparisons. Given the same visual and auditory features the new approach outperforms ICA, PCA and PLS methods both in precision and recall performance. Overall cross-media retrieval results between images and audios are very encouraging.

The Application of Cross-Media Retrieval Technology Based on Ontology

2015 ◽  
Vol 738-739 ◽  
pp. 1299-1302
Author(s):  
Hao Zhang ◽  
Gong Wen Xu ◽  
Wan Rong Guo ◽  
Ming Hai Liao ◽  
Chun Xiu Xu ◽  
...  
Keyword(s):  
Retrieval System ◽  
Retrieval Algorithm ◽  
Shape Features ◽  
Research Focus ◽  
Retrieval Method ◽  
Semantic Level ◽  
Cross Media ◽  
The Cross ◽  
Set Up ◽  
Representational Framework

As a large number of the multimedia information emerges, the cross-media retrieval system becomes an important research focus. The cross-media retrieval system is based on the traditional content retrieval, extracting color, texture, and shape features vector of the images. A new method was carried out in this paper. Firstly, the uniform semantic representational framework was built to organize the different mode media heterogeneous characteristics. Secondly, the Ontology database representing each type of media concepts was set up. The Ontology database organizes the low level features of the multimedia objects to associate multimedia files in the semantic level. Thirdly, the cross-media retrieval algorithm based on ontology was introduced. The results of the experiment showed that this cross-media retrieval method based on the Ontology was more effective and accurate.

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