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Multi-Modal Fusion Schemes for Image Retrieval Systems to Bridge the Semantic Gap

Multi-Modal Fusion Schemes for Image Retrieval Systems to Bridge the Semantic Gap

Nidhi Goel, Priti Sehgal
Copyright: © 2016 |Pages: 34
ISBN13: 9781466696853|ISBN10: 1466696850|EISBN13: 9781466696860
DOI: 10.4018/978-1-4666-9685-3.ch007
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MLA

Goel, Nidhi, and Priti Sehgal. "Multi-Modal Fusion Schemes for Image Retrieval Systems to Bridge the Semantic Gap." Emerging Technologies in Intelligent Applications for Image and Video Processing, edited by V. Santhi, et al., IGI Global, 2016, pp. 151-184. https://doi.org/10.4018/978-1-4666-9685-3.ch007

APA

Goel, N. & Sehgal, P. (2016). Multi-Modal Fusion Schemes for Image Retrieval Systems to Bridge the Semantic Gap. In V. Santhi, D. Acharjya, & M. Ezhilarasan (Eds.), Emerging Technologies in Intelligent Applications for Image and Video Processing (pp. 151-184). IGI Global. https://doi.org/10.4018/978-1-4666-9685-3.ch007

Chicago

Goel, Nidhi, and Priti Sehgal. "Multi-Modal Fusion Schemes for Image Retrieval Systems to Bridge the Semantic Gap." In Emerging Technologies in Intelligent Applications for Image and Video Processing, edited by V. Santhi, D. P. Acharjya, and M. Ezhilarasan, 151-184. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-9685-3.ch007

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Abstract

Image retrieval (IR) systems are used for searching of images by means of diverse modes such as text, sample image, or both. They suffer with the problem of semantic gap which is the mismatch between the user requirement and the capabilities of the IR system. The image data is generally stored in the form of statistics of the value of the pixels which has very little to do with the semantic interpretation of the image. Therefore, it is necessary to understand the mapping between the two modalities i.e. content and context. Research indicates that the combination of the two can be a worthwhile approach to improve the quality of image search results. Hence, multimodal retrieval (MMR) is an expected way of searching which attracts substantial research consideration. The main challenges include discriminatory feature extraction and selection, redundancy identification and elimination, information preserving fusion and computational complexity. Based on these challenges, in this chapter, authors focus on comparison of various MMR systems that have been used to improve the retrieval results.

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