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Register a new userContent based image retrieval cbir
استرجاع الصور بالاعتماد على المحتوى
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Added by
Tishreen University مشروع تخرج
Publication date
2013
fields
Software Engineering
and research's language is
العربية
Created by
Zein Shaheen
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هدفنا من خلال هذه الدراسة في إطار المشروع الفصلي للسنة الرابعة إلى إلقاء الضوء على استرجاع الصور من مجموعة كبيرة بالاعتماد على محتوى صورة هدف , و قمنا بتدعيم هذه الدراسة بتطبيق ضمن بيئة الماتلاب لبرنامج بحث عن الصور المشابهة لصورة مدخلة . و قد تركز بحثنا على ميزتين هامتين يكاد لا يخلو منها أي نظام بحث عن الصور بالاعتماد على المحتوى و هما ميزتي الهيستوغرام اللوني و بنية الصورة texture , ووضحنا الخطوات التي يتم في ضوئها عملية الاسترجاع بدءاً من تحليل الصورة و استخلاص شعاع الواصفات الخاص فيها , و مطابقته مع أشعة الميزات الخاصة بالصور الموجودة في قاعدة البيانات ليتم ترتيب الصور بحسب مدى تشابهها من الصورة الهدف . و تطرقت الدراسة إلى استخدام الفضاء اللوني HMMD كبديل للفضاء اللوني RGB لاستخراج واصفات البنية اللونية على اعتبار أنه نموذج لوني موجه بالمستخدم user oriented و بالتالي نضمن أن نحصل على نتائج أفضل ترضي المستخدم . وقمنا بتدعيم الدراسة بعدد من الأشكال و الأمثلة و المخططات التي توضح محتوى الدراسة النظرية و ما قمنا بعمله في التطبيق ضمن بيئة الماتلاب .
No English abstract
References used
John Eakins and Margaret Graham (1999) , "Content-based Image Retrieval" , University of Northumbria at Newcastle
Li, Ze-Nian, and Mark S. Drew. Fundamentals of multimedia, ISBN: 0130618721. Vol. 7458. Pearson Education, Inc., Upper Saddle River, NJ, 2004
Howarth, Peter, and Stefan Rüger. "Evaluation of texture features for content-based image retrieval." Image and Video Retrieval. Springer Berlin Heidelberg, 2004. 326-334
Kim, Hyeon Jun, and Jin Soo Lee. "HMMD color space and method for quantizing color using HMMD space and color spreading." U.S. Patent No. 6,633,407. 14 Oct. 2003
Huang, Yin-Fu, and He-Wen Chen. "A multi-type indexing CBVR system constructed with MPEG-7 visual features." Active Media Technology. Springer Berlin Heidelberg, 2011. 71-82
Ventura Royo, Carles. "Image-Based Query by Example Using MPEG-7 Visual Descriptors."
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Content Based Medical Image Retrieval (CBMIR) systems are a new technique which researchers aim to integrate with Computer Aided Diagnosis systems. These systems usually find and retrieve images from a large image-database which have a similar conten
t to a query image. Retrieval is done by extracting the visual features from the query image, formulating them in a features vector, comparing features vector components with those of the images in the database, and then, similarity measures are computed. Based on the similarity measures, images which have a similar content to the query image are retrieved. The introduced analysis study surveys and analyzes the current status of the CBMIR systems, evaluates our findings from this survey, and concludes some specific research directions in this field.
This research describes a system for recognition of handwritten
Arabic word without prior segmentation of the word into characters.
In this system, the recognition will be happened at two levels.
It is evolved basing on OCR (Optical Character Reco
gnition),
Hidden Markov Model, CBIR(Content Based Image Retrieval), it
also involves Mathematical Morphology.
Image captioning systems are expected to have the ability to combine individual concepts when describing scenes with concept combinations that are not observed during training. In spite of significant progress in image captioning with the help of the
autoregressive generation framework, current approaches fail to generalize well to novel concept combinations. We propose a new framework that revolves around probing several similar image caption training instances (retrieval), performing analogical reasoning over relevant entities in retrieved prototypes (analogy), and enhancing the generation process with reasoning outcomes (composition). Our method augments the generation model by referring to the neighboring instances in the training set to produce novel concept combinations in generated captions. We perform experiments on the widely used image captioning benchmarks. The proposed models achieve substantial improvement over the compared baselines on both composition-related evaluation metrics and conventional image captioning metrics.
Content based 2Dcerebral digital subtraction angiography(DSA) images retrieval
system has been built. The systemfinds and retrieves images fromcerebral DSA imagedatabase(
Cerebral Sacular Aneurysms) which have a similar content to a query image.
R
etrieval is done by extracting the visual shape features of cerebral saccular aneurysms
from a query image, formulating them in a feature vector, comparing feature vector
components with those of the cerebralDSA images in the database. Similarity measures
using Euclidian distanceare computed,based on the similarity measures, images which
have a similar content to the query image are retrieved. Resolution has been calculated by
finding the ratio between cerebral sacular aneurysm area in first retrieved image to cerebral
sacular aneurysm area in the query image for the eight query process which have been
done, average resolution was 98%. Results indicates that the designed content based image
retrieval could be used to calculate unknown cerebral saccular aneurysms area from a
cerebral saccular aneurysms database images whose areas are known.
Self-attention has recently been adopted for a wide range of sequence modeling problems. Despite its effectiveness, self-attention suffers from quadratic computation and memory requirements with respect to sequence length. Successful approaches to re
duce this complexity focused on attending to local sliding windows or a small set of locations independent of content. Our work proposes to learn dynamic sparse attention patterns that avoid allocating computation and memory to attend to content unrelated to the query of interest. This work builds upon two lines of research: It combines the modeling flexibility of prior work on content-based sparse attention with the efficiency gains from approaches based on local, temporal sparse attention. Our model, the Routing Transformer, endows self-attention with a sparse routing module based on online k-means while reducing the overall complexity of attention to O(n1.5d) from O(n2d) for sequence length n and hidden dimension d. We show that our model outperforms comparable sparse attention models on language modeling on Wikitext-103 (15.8 vs 18.3 perplexity), as well as on image generation on ImageNet-64 (3.43 vs 3.44 bits/dim) while using fewer self-attention layers. Additionally, we set a new state-of-the-art on the newly released PG-19 data-set, obtaining a test perplexity of 33.2 with a 22 layer Routing Transformer model trained on sequences of length 8192. We open-source the code for Routing Transformer in Tensorflow.1
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