Semantic image retrieval using multiple features
Nishant Singh,Shiv Ram Dubey,Pushkar Dixit,Jay Prakash Gupta +3 more
- 20 Jul 2012
pp 277-284
TL;DR: A new query-by-image technique using combination of multiple features that efficiently sifts through the dataset of images to retrieve semantically similar images.
read more
Abstract: In Content Based Image Retrieval (CBIR) some problem such as recognizing the similar images, the need for databases, the semantic gap, and retrieving the desired images from huge collections are the keys to improve. CBIR system analyzes the image content for indexing, management, extraction and retrieval via low-level features such as color, texture and shape. To achieve higher semantic performance, recent system seeks to combine the low-level features of images with high-level features that conation perceptual information for human beings. Performance improvements of indexing and retrieval play an important role for providing advanced CBIR services. To overcome these above problems, a new query-by-image technique using combination of multiple features is proposed. The proposed technique efficiently sifts through the dataset of images to retrieve semantically similar images.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Semantic Image Retrieval by Uniting Deep Neural Networks and Cognitive Architectures
Alexey Potapov,Innokentii Zhdanov,Oleg Scherbakov,Nikolai Skorobogatko,Hugo Latapie,Enzo Fenoglio +5 more
- 22 Aug 2018
TL;DR: This work proposes a hybrid solution consisting of a deep neural network for object detection and a cognitive architecture for query execution, which uses YOLOv2 and OpenCog to solve the retrieval of video frames containing objects of specified classes and specified spatial arrangement.
Combined global and local semantic feature–based image retrieval analysis with interactive feedback:
A Anandh,K Mala,R. Suresh Babu +2 more
TL;DR: This research work combines the interactive feedback to retrieve the user expected results by addressing the issues of semantic gap with modified binary wavelet transform, and provides better retrieval accuracy.
8
•Posted Content
Social and Business Intelligence Analysis Using PSO
TL;DR: From the experiments, the goal of this paper is to elaborate swarm intelligence for business intelligence decision making and the business rules management improvement and found that PSO is can facilitate the intelligence in social and business behavior.
Image Retrieval Based on Quad Chain Code and Standard Deviation
Tawfiq A. Al-assadi,Israa Hadi Ali +1 more
- 01 Jan 2014
TL;DR: A new idea for image retrieval depending on Quad chain code and standard deviation is presented and a matching operation is applied for each image in database to find what is the most matching image.
•Posted Content
Semantic Image Retrieval by Uniting Deep Neural Networks and Cognitive Architectures
Alexey Potapov,Innokentii Zhdanov,Oleg Scherbakov,Nikolai Skorobogatko,Hugo Latapie,Enzo Fenoglio +5 more
TL;DR: In this article, a hybrid solution consisting of a deep neural network for object detection and a cognitive architecture for query execution is proposed for video retrieval using YOLOv2 and OpenCog.
References
Textural Features Corresponding to Visual Perception
Hideyuki Tamura,Shunji Mori,Takashi Yamawaki +2 more
- 01 Jun 1978
TL;DR: The discrepancies between human vision and computerized techniques that are encountered in this study indicate fundamental problems in digital analysis of textures and could be overcome by analyzing their causes and using more sophisticated techniques.
2.5K
Object tracking using SIFT features and mean shift
Huiyu Zhou,Yuan Yuan,Chunmei Shi +2 more
TL;DR: Experimental work demonstrates that the proposed mean shift/SIFT strategy improves the tracking performance of the classical mean shift and SIFT tracking algorithms in complicated real scenarios.
685
Region-based image querying
Chad Carson,Serge Belongie,Hayit Greenspan,Jitendra Malik +3 more
- 20 Jun 1997
TL;DR: A new image representation is presented which provides a transformation from the raw pixel data to a small set of localized coherent regions in color and texture space based on segmentation using the expectation-maximization algorithm on combined color andtexture features.
Relevance feedback techniques in interactive content-based image retrieval
TL;DR: A relevance feedback based interactive retrieval approach, which effectively takes into account the above two characteristics in CBIR and greatly reduces the user's effort of composing a query and captures the users' information need more precisely.
248
Survey on Content Based Image Retrieval Systems
Yogita Mistry
- 01 Jan 2013
TL;DR: In this article, the authors explore the CBIR techniques and their usage in various application domains and explore the use of CBIR for browsing, searching and retrieving images from a large database of digital images.