As the growth and development of various multimedia technologies in the field of CBIR many advanced information retrieval systems have become popular and has brought the new evolution in fast and effective retrieval. In this paper the techniques of image classification in CBIR are been discussed and compared. It also introduces classifiers like support vector machine, Bayesian classifier for ac...

Image collections are growing at a rapid rate, motivating the need for efficient and effective tools to search through these databases. Content-based image retrieval (CBIR) techniques extract features directly from image data and use these to query image collections. In this paper, we focus on some more advanced issues in CBIR, namely the extraction of image features from compressed images, the...

In this paper we address several aspects of the learning problem in content-based image retrieval (CBIR). First, we introduce the linear and kernel-based biased discriminant analysis, or BiasMap, to fit the unique nature of relevance feedback as a small sample biased classification problem. Secondly, a WARF (word association via relevance feedback) formula is presented for learning keyword rela...

We describe a setup and experiments where users are checking and correcting image tags given by an automatic tagging system. We study how much the application of a content-based image retrieval (CBIR) method speeds up the process of finding and correcting the erroneously-tagged images. We also analyze the use of implicit relevance feedback from the user’s gaze tracking patterns as a method for ...

The substance based picture recovery (CBIR) is a standout amongst the most famous, climbing exploration zones of the advanced picture handling. In this system, pictures are physically commented with catchphrases and afterward recovered utilizing content based hunt systems. The objective of CBIR is to concentrate visual substance of a picture immediately, for example color, surface, or shape. Th...

Color and texture are the important features used in Content-based image retrieval (CBIR) systems. CBIR is a process that searches and retrieves images from large image databases. To perform this operation, CBIR requires color, texture and shape features of images. In this paper, color and texture features of images are considered. First order statistics and run-length characteristics of images...

Automatic image annotation (AIA) is expected to be a promising way to improve the performance of content-based image retrieval (CBIR). However, current image annotation results are always incomplete and noisy, and far from practical usage. In this paper, we incorporate semantic annotations into CBIR via query expansion scheme to improve retrieval accuracy. In the proposed method, semantic annot...

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 perform...

One of the challenging issues in managing the existing large digital image libraries and databases is Content Based Image Retrieval (CBIR). The accuracy of image retrieval methods in CBIR is subject to effective extraction of image features such as color, texture, and shape. In this paper, we propose a new image retrieval method using contourlet transform coefficients to index texture of the im...

In this paper, we propose the concept of content-based image retrieval (CBIR) and demonstrate its potential use in picture archival and communication system (PACS). We address the importance of image retrieval in PACS and highlight the drawbacks existing in traditional textual-based retrieval. We use a digital mammogram database as our testing data to illustrate the idea of CBIR, where retrieva...