Multimodal Image Retrieval Based on Annotation Keywords and Visual Content

Song, Haiyu; Li, Xiongfei; Wang, Pengjie

doi:10.1109/case.2009.60

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…This technique is not suitable for two reasons, first it puts heavy labor task on the human during image annotation; when the database is extremely large. Second here image annotation is based on the annotator's subjective feelings and background knowledge, which often does not meet the user's demands [4]. Due to this reason, the text based retrieval needs to adopt visual based image retrieval techniques for further analysis.…”

Section: Content-based Image Retrieval Backgroundmentioning

confidence: 99%

Complementary semantic model for content-based image retrieval

Kumar

Hussain

et al. 2014

2014 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing(ICCWAMTIP)

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Multimedia technology is known as the single repository source for information retrieval that includes images, audio and video. Nowadays, it is growing with tremendous amount in the field of communication technology. Traditional text based retrieval techniques of information retrieval are inefficient at searching user's intended information from the large multimedia database. To address this problem, content based image retrieval techniques are applied for information retrieval. Content-based image retrieval has been hot topic from past two decades by many researchers' community. This topic covers many fields such as image processing, artificial intelligence, pattern recognition, and data mining and beside other related area. In this paper image retrieval techniques on the basis of color, texture and shape are explained, studies and compared. While relevance feedback technique, system accuracy, efficiency have also been discussed. Emphasize on the concept of 'semantic-based image retrieval techniques' have been proposed in this paper, which is better technique to overcome the problem of 'semantic gap' in the context of image retrieval.

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Section: Content-based Image Retrieval Backgroundmentioning

confidence: 99%

Complementary semantic model for content-based image retrieval

Kumar

Hussain

et al. 2014

2014 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing(ICCWAMTIP)

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“…Image visual information is used to improve image retrieval results in [31,32]. Pham et al [31] have studied a method to annotate image automatically and retrieval of multimedia document using the outcome of latent semantic analysis (LSA).…”

Section: Annotation-based Image Retrievalmentioning

confidence: 99%

“…Image visual features are incorporated to improve image retrieval results. Initial retrieval results are obtained by keyword relevance model for given input query [32]. Furthermore, this retrieval result is refined with region feature vector.…”

Section: Annotation-based Image Retrievalmentioning

confidence: 99%

Image recommendation based on keyword relevance using absorbing Markov chain and image features

Sejal

Rashmi

Venugopal

et al. 2016

Int J Multimed Info Retr

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Image recommendation is an important feature of search engine, as tremendous amount of images are available online. It is necessary to retrieve relevant images to meet the user's requirement. In this paper, we present an algorithm image recommendation with absorbing Markov chain (IRAbMC) to retrieve relevant images for a user's input query. Images are ranked by calculating keyword relevance probability between annotated keywords from log and keywords of user input query. Keyword relevance is computed using absorbing Markov chain. Images are reranked using image visual features. Experimental results show that the IRAbMC algorithm outperforms Markovian semantic indexing (MSI) method with improved relevance score of retrieved ranked images.

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“…Region level annotation [43,44], requires segmenting the image into objects, regions, or blobs, and annotating each region. Region level annotation allows for direct object searching and in most cases, produces higher retrieval accuracy since what is inside the image can be better represented [45].…”

Section: Abstract-based Annotationmentioning

confidence: 99%

A Literature Review of Image Retrieval based On Semantic Concept

Riad¹,

Elminir²,

Abdel-Ghany³

2012

IJCA

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This paper attempts to provide a comprehensive review and characterize the problem of the semantic gap that is the key problem of content-based image retrieval and the current attempts in high-level semantic-based image retrieval being made to bridge it. Major recent publications are included in this review covering different aspects of the research in the area of high-level semantic features. In this paper the different methods of image retrieval systems are described and major categories of the state-of-the-art techniques in narrowing down the "semantic gap" are presented. Finally, based on existing technologies and the demand from real-world applications, a few promising future research directions are suggested.

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Multimodal Image Retrieval Based on Annotation Keywords and Visual Content

Cited by 8 publications

References 9 publications

Complementary semantic model for content-based image retrieval

Complementary semantic model for content-based image retrieval

Image recommendation based on keyword relevance using absorbing Markov chain and image features

A Literature Review of Image Retrieval based On Semantic Concept

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