Combination of Term Weighting and Integrated Color Intensity Co-occurrence Matrix for Two-Level Image Retrieval on Social Media Data

Siradjuddin, Indah Agustien; Bahruddin, Reza Pahlevi; Sophan, Mochammad Kautsar

doi:10.1016/j.procs.2019.08.174

Cited by 4 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…And a mapping of the medical text of queries and documents into concepts and then applying a concept-selection method to keep only the most significant concepts is proposed for retrieval of medical images. [28] The two-level image retrieval that combines the text and image-based query for image retrieval is performed. In the text-based retrieval, The text and document pre-processing, weighting the word from the dictionary, based on TF-IDF Model & the similarity is calculated.…”

Section: Discussionmentioning

confidence: 99%

Image Retrieval Through Free-Form Query using Intelligent Text Processing

Angadi¹,

Purad²

2023

IJITEE

View full text Add to dashboard Cite

Image Retrieval is the process of retrieving images from the image/multimedia databases. Retrieval of images are carried out with various types of queries, free-form query is a text-query that consists of single or multiple keywords and/or concepts or descriptions of images with or without the inclusion of wild-card characters and/or punctuations. This work aims to handle image retrieval based on free-form text queries. Simple & complex queries of conceptual descriptions of images are explored and an intelligent processing system with free-form queries based on the Bag-of-Words model is modified and built for natural scene images and on Diverse Social Images using the Damerau-Levenshtein edit distance measure. The efficacy of the proposed system is evaluated by testing 1500 free-form text queries and has resulted in a recall accuracy of 91.3% on natural scene images (of Wang/Corel database) and 100% on Diverse Social Images (of DIV400 dataset). These results show that the system proposed has produced satisfactory performance compared to published results such as the harmonic mean of precision and recall (i.e. F1-Score) of 76.70% & 63.32% at retrieval of 20 images etc in reported works.

show abstract

Section: Discussionmentioning

confidence: 99%

Image Retrieval Through Free-Form Query using Intelligent Text Processing

Angadi¹,

Purad²

2023

IJITEE

View full text Add to dashboard Cite

show abstract

“…However, all of these studies were only applied to the Panjang document. There has been no research on the application of this TF-IDF word weighting method for short documents [18]. Previous research on short documents did not use this term weighting.…”

Section: Related Workmentioning

confidence: 99%

Term Weighting Based Indexing Class and Indexing Short Document for Indonesian Thesis Title Classification

Ni'mah

Syuhada²

2022

J-Cosine

View full text Add to dashboard Cite

Document classification nowadays is an easy thing to do because there are the latest methods to get maximum results. Document classification using the term weighting TF-IDF-ICF method has been widely studied. Documents used in this research generally use large documents. If the term weighting TF-IDF method is used in a short text document such as the Thesis Title, the document will not get a perfect score from the classification results. Because in the IDF will calculate the weight of words that always appear to be few, ICF will calculate the weight of words that often appear in the class to be few. While the word should have great weight to be the core of a short text document. Therefore, this study aims to conduct research on word weighting based on class indexation and short document indexation, namely TF-IDF-ICF-IDSF. This study uses a classification comparison Naïve Bayes and SVM. The dataset used is Thesis Title of Informatics Education student at Trunojoyo Madura University. The test results show that the classification results using the TF-IDF-ICF-IDSF term weighting method outperform other term weighting, namely getting 91% Precision, 93% Recall, 86% F1-Score, and 84% Accuracy on SVM.

show abstract

RETRACTED ARTICLE: Combination Weighting Method of Engineering Disciplines Evaluation Index Based on Soft Computing

Shuang

Ding

2022

IETE Journal of Research

View full text Add to dashboard Cite

Combination of Term Weighting and Integrated Color Intensity Co-occurrence Matrix for Two-Level Image Retrieval on Social Media Data

Cited by 4 publications

References 7 publications

Image Retrieval Through Free-Form Query using Intelligent Text Processing

Image Retrieval Through Free-Form Query using Intelligent Text Processing

Term Weighting Based Indexing Class and Indexing Short Document for Indonesian Thesis Title Classification

RETRACTED ARTICLE: Combination Weighting Method of Engineering Disciplines Evaluation Index Based on Soft Computing

Contact Info

Product

Resources

About