Content Based Image Retrieval by Integration of Metadata Encoded Image and Text Features (1, 2)

Image retrieval using the combination of text-based and content-based algorithms

Image retrieval is an important research field which has received great attention in the last decades. In this paper, we present an approach for the image retrieval based on the combination of text-based and content-based features. For text-based features, keywords and for content-based features, color and texture features have been used. Query in this system contains some keywords and an input...

CONTENT BASED IMAGE RETRIEVAL BY INTEGRATION OF METADATA ENCODED MULTIMEDIA (IMAGE AND TEXT) FEATURES IN CONSTRUCTING A VIDEO SUMMARIZER APPLICATION Ramprasad Anusuri, B.Tech. Problem in Lieu of Thesis Prepared for the Degree of MASTER OF SCIENCE UNIVERSITY OF NORTH TEXAS

تأملاتی بر نمایه‌ سازی تصاویر: یک تصویر ارزشی برابر با هزار واژه

Purpose: This paper presents various  image indexing techniques and discusses their advantages and limitations.             Methodology: conducting a review of the literature review, it identifies three main image indexing techniques, namely concept-based image indexing, content-based image indexing and folksonomy. It then describes each technique. Findings: Concept-based image indexing is te...

Semiautomatic Image Retrieval Using the High Level Semantic Labels

Content-based image retrieval and text-based image retrieval are two fundamental approaches in the field of image retrieval. The challenges related to each of these approaches, guide the researchers to use combining approaches and semi-automatic retrieval using the user interaction in the retrieval cycle. Hence, in this paper, an image retrieval system is introduced that provided two kind of qu...

A Novel Method for Content Base Image Retrieval Using Combination of Local and Global Features

Content-based image retrieval (CBIR) has been an active research topic in the last decade. In this paper we proposed an image retrieval method using global and local features. Firstly, for local features extraction, SURF algorithm produces a set of interest points for each image and a set of 64-dimensional descriptors for each interest points and then to use Bag of Visual Words model, a cluster...