Learning a hybrid similarity measure for image retrieval

Wu, Jun; Shen, Hong; Li, Yidong; Xiao, Zhibo; Lu, Mingyu; Wang, Chunli

doi:10.1016/j.patcog.2013.04.008

Cited by 51 publications

(24 citation statements)

References 36 publications

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“…J. Wu, H. Shen, Y. Li, Z. B. Xiao, M. Y. Lu and C. L.Wang have proposed, "Learning a hybrid similar it measure for image retrieval" [5]In the system they have used to search images by description and content based are used in the procedure of image retrieval process. It improve query value and image relevancy in search process initiated using the user intention image selection process.…”

Section: Literature Reviewmentioning

confidence: 99%

Image Annotation Using Multi-Label Learning

.¹

2017

IJRASET

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Recently Ayurveda science is gaining more importance and popularity across the globe because of its amazing therapeutic values. In olden days man lived very close to nature and whenever he got indisposed he cured himself by the resources and materials provided by nature, which is nothing but the herbs. Unfortunately, due to lack of scientific validation in various concepts, this precious gift from our ancestors is trailing. Hence, evidence-based research is highly needed for global recognition and acceptance of Ayurveda, which needs further advancements in the research methodology. Image Annotation is the procedure by which computer system automatically assign data about it in the form of keywords to digital image. With the rapid progresses of information technologies, may work have been dedicated to applying the technologies of pattern recognition and image processing to object identification i.e. leaf identification in our project. The proposed system aims to describe, desired leaf identification base on shape. In our project we describe the development of an android application that gives user ability to identify the ayurvedic plant leaf base on photographs of desired leaf. The propose system is based on image annotation, where we shall apply pattern recognition and image processing on ayurvedic plants leaves. The proposed system then use to identity plant and shows its medical features by using capture image of plant leaves from android application. The project is useful for researchers who are working in ayurvedic field, farmers and also for community to spread awareness about surrounding ayurvedic plants.

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Section: Literature Reviewmentioning

confidence: 99%

Image Annotation Using Multi-Label Learning

.¹

2017

IJRASET

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“…However, recent studies have shown that there is a semantic gap between low-level visual feature for representing images in Content-based image retrieval and the high-level semantic tags for describing image content. The semantic gap is linking through the automatic image annotation that captures semantic features with machine learning techniques [9][10]. Many algorithms have been developed for tag based image retrieval to overcome the limitation of Content-based image retrieval.…”

Section: Review Workmentioning

confidence: 99%

“…The relevant image is searched by similarities between the image tags and the textual query. However recent research shows that, retrieving the relevant images by TBIR is efficient than CBIR [10]. Tag ranking intends a ranking function that place relevant tags in front of the irrelevant ones.…”

Section: Review Workmentioning

confidence: 99%

A Proposed Method for Semantic Annotation on Social Media Images

Ghosh¹,

Bandyopadhyay²

2017

IJECS

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Semantic annotation attaches to various concepts (e.g. people, things, places, organizations etc) in any other content. A semantically annotated document is easy to interpret, combine and reuse by computers. In human history Web plays as the greatest information source. Many researchers believe semantic annotations can be inserted in web-based documents for information extraction and knowledge mining. These annotations use terms defined in an ontology. This paper uses the annotation process based on textual patterns for improving annotation. IntroductionImage databases are large and databases are used in public domain applications and domain-specific applications. Internet services such as social networks, photo sharing image and video monitoring are the specific examples. Specific-domain applications use medicine, aerial surveillance, audiovisual archiving and many more.

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“…From the perspective of long-term learning, the judgments provided by different users are stored in log files, from which the semantic correlations among database images could be inferred. Previous studies have shown that exploiting the semantic clues hidden in query logs is helpful to improve search effectiveness and efficiency [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%