scholarly journals Image Retrieval Based on Texton Frequency-Inverse Image Frequency

2020 ◽  
pp. 87-94
Author(s):  
Yufis Azhar ◽  
Agus Eko Minarno ◽  
Yuda Munarko ◽  
Zaidah Ibrahim
Keyword(s):  
Image Retrieval ◽  
Inverse Image ◽  
Document Retrieval ◽  
Previous Method ◽  
Feature Weighting ◽  
Frequency Method ◽  
Term Weighting ◽  
Image Frequency

In image retrieval, the user hopes to find the desired image by entering another image as a query. In this paper, the approach used to find similarities between images is feature weighting, where between one feature with another feature has a different weight. Likewise, the same features in different images may have different weights. This approach is similar to the term weighting model that usually implemented in document retrieval, where the system will search for keywords from each document and then give different weights to each keyword. In this research, the method of weighting the TF-IIF (Texton Frequency-Inverse Image Frequency) method proposed, this method will extract critical features in an image based on the frequency of the appearance of texton in an image, and the appearance of the texton in another image. That is, the more often a texton appears in an image, and the less texton appears in another image, the higher the weight. The results obtained indicate that the proposed method can increase the value of precision by 7% compared to the previous method.

scholarly journals RANDOM WALK TERM WEIGHTING FOR IMPROVED TEXT CLASSIFICATION

2007 ◽  
Vol 01 (04) ◽  
pp. 421-439 ◽  
Author(s):  
SAMER HASSAN ◽  
RADA MIHALCEA ◽  
CARMEN BANEA
Keyword(s):  
Random Walk ◽  
Text Classification ◽  
Feature Weighting ◽  
Random Walk Model ◽  
Term Weighting ◽  
New Approach ◽  
Standard Classification ◽  
Term Weights ◽  
Traditional Term ◽  
Word Feature

This paper describes a new approach for estimating term weights in a document, and shows how the new weighting scheme can be used to improve the accuracy of a text classifier. The method uses term co-occurrence as a measure of dependency between word features. A random walk model is applied on a graph encoding words and co-occurrence dependencies, resulting in scores that represent a quantification of how a particular word feature contributes to a given context. Experiments performed on three standard classification datasets show that the new random walk based approach outperforms the traditional term frequency approach of feature weighting.

scholarly journals Optimasi Pembobotan pada Query Expansion dengan Term Relatedness to Query-Entropy based (TRQE)

2015 ◽  
Vol 6 (3) ◽  
Author(s):  
Resti Ludviani ◽  
Khadijah F. Hayati ◽  
Agus Zainal Arifin ◽  
Diana Purwitasari
Keyword(s):  
Query Expansion ◽  
Retrieval System ◽  
Document Retrieval ◽  
Retrieval Performance ◽  
Term Weighting ◽  
New Approach ◽  
Term Selection ◽  
Relevance Evaluation ◽  
Feedback Module ◽  
Pseudo Feedback

Abstract. An appropriate selection term for expanding a query is very important in query expansion. Therefore, term selection optimization is added to improve query expansion performance on document retrieval system. This study proposes a new approach named Term Relatedness to Query-Entropy based (TRQE) to optimize weight in query expansion by considering semantic and statistic aspects from relevance evaluation of pseudo feedback to improve document retrieval performance. The proposed method has 3 main modules, they are relevace feedback, pseudo feedback, and document retrieval. TRQE is implemented in pseudo feedback module to optimize weighting term in query expansion. The evaluation result shows that TRQE can retrieve document with the highest result at precission of 100% and recall of 22,22%. TRQE for weighting optimization of query expansion is proven to improve retrieval document.     Keywords: TRQE, query expansion, term weighting, term relatedness to query, relevance feedback Abstrak..Pemilihan term yang tepat untuk memperluas queri merupakan hal yang penting pada query expansion. Oleh karena itu, perlu dilakukan optimasi penentuan term yang sesuai sehingga mampu meningkatkan performa query expansion pada system temu kembali dokumen. Penelitian ini mengajukan metode Term Relatedness to Query-Entropy based (TRQE), sebuah metode untuk mengoptimasi pembobotan pada query expansion dengan memperhatikan aspek semantic dan statistic dari penilaian relevansi suatu pseudo feedback sehingga mampu meningkatkan performa temukembali dokumen. Metode yang diusulkan memiliki 3 modul utama yaitu relevan feedback, pseudo feedback, dan document retrieval. TRQE diimplementasikan pada modul pseudo feedback untuk optimasi pembobotan term pada ekspansi query. Evaluasi hasil uji coba menunjukkan bahwa metode TRQE dapat melakukan temukembali dokumen dengan hasil terbaik pada precision  100% dan recall sebesar 22,22%.Metode TRQE untuk optimasi pembobotan pada query expansion terbukti memberikan pengaruh untuk meningkatkan relevansi pencarian dokumen.Kata Kunci: TRQE, ekspansi query, pembobotan term, term relatedness to query, relevance feedback

scholarly journals An improved multiple-frequency method for measuring in situ target strengths

2005 ◽  
Vol 62 (8) ◽  
pp. 1636-1646 ◽  
Author(s):  
Stéphane G. Conti ◽  
David A. Demer ◽  
Michael A. Soule ◽  
Jean H.E. Conti
Keyword(s):  
Reference System ◽  
Three Dimensional ◽  
Previous Method ◽  
Target Strength ◽  
System Transformation ◽  
Multiple Frequency ◽  
Frequency Method ◽  
Multiple Targets ◽  
Common Reference ◽  
The Individual

Abstract Refinements have been made to the multiple-frequency method for rejecting overlapping echoes when making target-strength measurements with split-beam echosounders described in Demer et al. (1999). The technique requires that echoes, simultaneously detected with two or more adjacent split-beam transducers of different frequencies, pass multiple-target rejection algorithms at each frequency, and characterize virtually identical three-dimensional target coordinates. To translate the coordinates into a common reference system for comparison, the previous method only considered relative transducer positions and assumed that the beam axes of the transducers were parallel. The method was improved by first, optimizing the accuracy and precision of the range and angular measurements of the individual frequency detections; and second, precisely determining acoustically the relative positions and angular orientations of the transducers, thus completely describing the reference-system transformation(s). Algorithms are presented for accurately and precisely estimating the transformation parameters, and efficiently rejecting multiple targets while retaining measurements of most single targets. These improvements are demonstrated through simulations, controlled test-tank experiments, and shipboard measurements using 38- and 120-kHz split-beam transducers. The results indicate that the improved multiple-frequency TS method can reject more than 97% of multiple targets, while allowing 99% of the resolvable single targets to be measured.

scholarly journals Arabic Book Retrieval using Class and Book Index Based Term Weighting

2017 ◽  
Vol 7 (6) ◽  
pp. 3705 ◽  
Author(s):  
M. Ali Fauzi ◽  
Agus Zainal Arifin ◽  
Anny Yuniarti
Keyword(s):  
Feature Selection ◽  
Information Retrieval ◽  
Feature Space ◽  
Previous Method ◽  
Term Weighting ◽  
Weighting Method ◽  
Automatic Retrieval ◽  
User Query ◽  
Indexing Method ◽  
F Measure

One of the most common issue in information retrieval is documents ranking. Documents ranking system collects search terms from the user and orderly retrieves documents based on the relevance. Vector space models based on TF.IDF term weighting is the most common method for this topic. In this study, we are concerned with the study of automatic retrieval of Islamic <em>Fiqh</em> (Law) book collection. This collection contains many books, each of which has tens to hundreds of pages. Each page of the book is treated as a document that will be ranked based on the user query. We developed class-based indexing method called inverse class frequency (ICF) and book-based indexing method inverse book frequency (IBF) for this Arabic information retrieval. Those method then been incorporated with the previous method so that it becomes TF.IDF.ICF.IBF. The term weighting method also used for feature selection due to high dimensionality of the feature space. This novel method was tested using a dataset from 13 Arabic Fiqh e-books. The experimental results showed that the proposed method have the highest precision, recall, and F-Measure than the other three methods at variations of feature selection. The best performance of this method was obtained when using best 1000 features by precision value of 76%, recall value of 74%, and F-Measure value of 75%.

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