We present a framework for active learning in the multiple-instance (MI) setting. In an MI learning problem, instances are naturally organized into bags and it is the bags, instead of individual instances, that are labeled for training. MI learners assume that every instance in a bag labeled negative is actually negative, whereas at least one instance in a bag labeled positive is actually posit...

In this paper a prototype system is described for the management and content-based retrieval of defect images in huge image databases. This is a real problem in surface inspection applications, since modern inspection systems may produce up to thousands of defect images in a day. The retrieval is based on shape and internal structure characteristics of defects, so no manual labeling nor annotat...

This article provides a framework to describe and compare content-based image retrieval systems. Sixteen contemporary systems are described in detail, in terms of the following technical aspects: querying, relevance feedback, result presentation, features, and matching. For a total of 44 systems we list the features that are used. Of these systems, 35 use any kind of color features, 28 use text...

Relevance feedback methods for content-based image retrieval have shown promise in a variety of image database applications. These techniques assume two-class relevance feedback: relevant and irrelevant classes. While simple computationally, two-class relevance feedback often becomes inadequate in providing sufficient information to help rapidly improve retrieval performance. In this paper we p...

A new method for content-based image retrieval is being presented. This method uses a vector-space model to represent images in a multi-dimensional space. This model allows the use of multiple attributes in the retrieval process and also identiies the most selective values for each attribute. Therefore by ignoring the less signiicant values the user can reduce the dimensionality of the feature ...

This paper considered performance of content based image retrieval system using image representation nonparametric algorithms. Performance comparison of Epanechnikov, Gaussian and Histogram non-parametric algorithms was done in a generic image retrieval system. Chan & Vese and Cosine Angle Distance algorithms were used for segmentation and similarity matching respectively. The performance of th...

This paper presents a planar shape recognition method for content based image retrieval in multimedia databases. Shapes are characterised by their corners, which are extracted from the curvature function of their contours. Then, corner sequences are compared by using Hidden MarkovModels so that the process is resistant against corner shifting and loss. The proposed method has been successfully ...

This contribution develops a new technique for content-based image retrieval. Where most existing image retrieval systems mainly focus on color and color distribution or texture, we classify the images based on local invariants. These features represent the image in a very compact way and allow fast comparison and feature matching with images in the database. Using local features makes the syst...

Shape is one of the main features in content based image retrieval (CBIR). This paper proposes a new shape signature. In this technique, features of each shape are extracted based on four sides of the rectangle that covers the shape. The proposed technique is Fourier based and it is invariant to translation, scaling and rotation. The retrieval performance between some commonly used Fourier base...

In this paper, we present an ongoing work aiming to improve content based image retrieval performance with the help of logical concept analysis. Domain semantic is formalized and used instead of classical CBIR visual features. This is being applied to comicbooks using Sewelis.