Using visual and text features for direct marketing on multimedia messaging services domain

Battiato, Sebastiano; Farinella, Giovanni Maria; Giuffrida, Giovanni; Sismeiro, Catarina; Tribulato, Giuseppe

doi:10.1007/s11042-008-0250-z

Cited by 11 publications

(7 citation statements)

References 23 publications

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“…These enable companies to deliver unified information and product value to the public (Chang, 2009). Among previous studies, Battiato et al (2009) used a multimedia messaging service (MMS) to send advertisements to cell phones and track consumer behavior, intending to boost the sales personnel's performance results. The use of MMS in marketing is not only convenient but also effective to a certain extent.…”

Section: Multimedia Marketingmentioning

confidence: 99%

Decision-Making Models for Using Multimedia Marketing in Enterprises

Hsieh¹,

Hsiung²

2020

JSSH

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This study has three purposes: to understand the enterprises’ expectations and needs of using multimedia marketing, to examine the reasons behind enterprises’ rejection of multimedia marketing, and to verify whether multimedia marketing meets the expectations of the enterprises. This study combines the technology acceptance model (TAM) and expectation confirmation theory (ECT) to investigate Taiwanese enterprises’ perspectives of using multimedia marketing by employing the questionnaire technique. A total of 288 valid paper questionnaires (143 companies did not use multimedia marketing, while 145 did) were collected after eliminating invalid ones. As a result, this study shows that TAM and ECT are suitable models for explaining the enterprises’ behavior of using multimedia marketing. Perceived ease of use is an important factor for enterprises that do not use multimedia marketing. In addition, both perceived ease of use and perceived usefulness are important factors that affect the enterprises’ continuance intention to adopt multimedia marketing.

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Section: Multimedia Marketingmentioning

confidence: 99%

Decision-Making Models for Using Multimedia Marketing in Enterprises

Hsieh¹,

Hsiung²

2020

JSSH

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“…Early works on feature learning include single feature learning [17,18] and multimodal feature learning (image and text [19,20], image and audio [21]). In the aspect of feature learning, Jian et al [28] exploit Quaternionic Distance Based Weber Descriptor for detecting outliers in color image and apply hierarchical scheme for object detection.…”

Section: Related Workmentioning

confidence: 99%

Semi-supervised Cross-media Feature Learning via Efficient L2,q Norm

2019

KSII TIIS

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With the rapid growth of multimedia data, research on cross-media feature learning has significance in many applications, such as multimedia search and recommendation. Existing methods are sensitive to noise and edge information in multimedia data. In this paper, we propose a semi-supervised method for cross-media feature learning by means of L 2,q norm to improve the performance of cross-media retrieval, which is more robust and efficient than the previous ones. In our method, noise and edge information have less effect on the results of cross-media retrieval and the dynamic patch information of multimedia data is employed to increase the accuracy of cross-media retrieval. Our method can reduce the interference of noise and edge information and achieve fast convergence. Extensive experiments on the XMedia dataset illustrate that our method has better performance than the state-of-the-art methods.

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“…The first kind of methods are designed for single-media (image [17], [18]) or multi-modal (image and text [19], [20], [21], image and audio [22]) applications. The second kind of methods can learn cross-media correlations among different media types.…”

Section: Related Workmentioning

confidence: 99%

“…Exploiting such coherence can enhance the effect of image feature representation. (2) Image and text feature learning: Battiato et al [19], [20] jointly analyze visual features and text information, so as to improve not only the effect, but also the speed of learning process in multimedia messaging selecting. Znaidia et al [21] propose the bag-ofmultimedia-words model, which combines the heterogeneous information of both the text and the pixel-based information of a multimedia document.…”

Section: Related Workmentioning

confidence: 99%

Semi-Supervised Cross-Media Feature Learning With Unified Patch Graph Regularization

Peng

Zhai

Zhao

et al. 2016

IEEE Trans. Circuits Syst. Video Technol.

133

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With the rapid growth of multimedia data such as text, image, video, audio and 3D model, cross-media retrieval has become increasingly important, because users can retrieve the results with various types of media by submitting a query of any media type. Comparing with single-media retrieval such as image retrieval and text retrieval, cross-media retrieval is better because it provides the retrieval results with all kinds of media at the same time. In this paper, we focus on how to learn cross-media features for different media types, which is a key challenge for cross-media retrieval. Existing methods either model different media types separately or only exploit the labeled multimedia data. Actually, data from different media types with the same semantic category are complementary to each other, and jointly modeling them is able to improve the accuracy of cross-media retrieval. In addition, although the labeled data are accurate, they require a lot of human labor and thus are very scarce. To address the above problems, we propose a semi-supervised crossmedia feature learning algorithm with unified patch graph regularization (S 2 UPG). Our motivation and contributions mainly lie in the following three aspects: (1) Existing methods only model different media types in different graphs, while we employ one joint graph to simultaneously model all the media types. The joint graph is able to fully exploit the semantic correlations among various media types, which are complementary to provide the rich hint for cross-media correlation. (2) Existing methods only consider the original media instances (such as images, videos, texts, audios, and 3D models) but ignore their patches, while we make full use of both the media instances and their patches in one graph. Cross-media patches could emphasize the important parts and make cross-media correlations more precise.(3) Traditional semi-supervised learning methods only exploit single-media unlabeled instances, while our approach fully exploits cross-media unlabeled instances and their patches, which can increase the diversity of training data and boost the accuracy of cross-media retrieval. Comparing with the current state-of-theart methods on 3 datasets, including the challenging XMedia dataset with 5 media types, the comprehensive experimental results show that our proposed approach performs better.

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Using visual and text features for direct marketing on multimedia messaging services domain

Cited by 11 publications

References 23 publications

Decision-Making Models for Using Multimedia Marketing in Enterprises

Decision-Making Models for Using Multimedia Marketing in Enterprises

Semi-supervised Cross-media Feature Learning via Efficient L2,q Norm

Semi-Supervised Cross-Media Feature Learning With Unified Patch Graph Regularization

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