A Review: Relating Low Level Features to High Level Semantics in CBIR

Upadhyaya, Nikita; Dixit, Manish

doi:10.14257/ijsip.2016.9.3.37

Cited by 15 publications

(12 citation statements)

References 16 publications

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“…In 2016, to reduce the semantic gap between High-Level Features such as text, visual features and Low-Level Features like colour, shape and texture five methods were studied. Two of which were the use of machine learning tools to associate LL features with query concepts and use of object ontology to define high-level concepts [5]. Use of object ontology works in a way by assigning a region attribute such as an image of scenery may contain grass in the lower region.…”

Section: Literature Backgroundmentioning

confidence: 99%

Image to Image Search using K-means Clustering

Kumar¹,

Kumar²,

Kashyap³

2019

IJCA

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With the increase in the amount of information on the World Wide Web, it has become a difficult task of finding relevant information. More often, textual based search engines were used but there were very few search engines using which similar images can be searched all over the internet with an image as input. This method of finding information is called Content-Based Image Retrieval (CBIR). Commonly used engines for CBIR like Google Images, Yahoo! Images are based on textual annotation of Images. The images that form the result have to be previously tagged in order to appear as result. In this research, K-means clustering for segmentation of images into clusters have been studied and applied, thereby finding similar images without the need of labelling or tagging images in the database.

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Section: Literature Backgroundmentioning

confidence: 99%

Image to Image Search using K-means Clustering

Kumar¹,

Kumar²,

Kashyap³

2019

IJCA

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“…Here, Wang image database [4] [6] [20] [23] is employed for our proposed approach. The experimental analysis is done on 1-k images of Wang database with 10 categories of image classes like African, Beach, Monuments, Busses, Dinosaurs, Elephants, Flowers, Horses, Mountain and food.…”

Section: G Image Databasementioning

confidence: 99%

A Refined Approach of Image Retrieval Using RBF-SVM Classifier

Ansari¹,

Dixit²

2017

IJSIP

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“…Nikita Upadhyaya and Manish Dixit (2016) presented a review depicting the importance between low-level features and high-level semantics in the field of CBIR .The research work explained the working of CBIR framework along with the categories of CBIR queries. A detailed explanation of various semantic retrieval techniques was also presented for better research study [4].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Approach towards Satellite Image Retrieval using Semantic Mining with Hashing

Mary¹,

Kiran²

2016

IJAIS

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Satellite images have gained a wide popularity in the field of content-based image retrieval. A massive amount of these images are collected every year due to the high availability of satellites and computer technologies. However, extracting user-specific content from these images still remains a primary concern due to the presence of semantic gap. This limits the capabilities of CBIR. Therefore, an effective and efficient method is required for image retrieval. This paper puts forward a semantic-based image retrieval approach along with the advantages of hashing for better feature extraction and precise retrieval. Hashing accelerates the quality of similarity search among images by generating unique imagehash codes .This approach also aims to scale down the problems related to semantic gap for better retrieval results. General TermsRemote sensing, Satellites, Data sets, Content based image retrieval and Algorithms.

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A Review: Relating Low Level Features to High Level Semantics in CBIR

Abstract: Abstract

Cited by 15 publications

References 16 publications

Image to Image Search using K-means Clustering

Image to Image Search using K-means Clustering

A Refined Approach of Image Retrieval Using RBF-SVM Classifier

An Efficient Approach towards Satellite Image Retrieval using Semantic Mining with Hashing

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