This paper examines the problem of image retrieval from large, heterogeneous image databases. We present a technique that fulfills several needs identified by surveying recent res...
We propose a new Bayesian approach to object-based image retrieval with relevance feedback. Although estimating the object posterior probability density from few examples seems in...
Derek Hoiem, Rahul Sukthankar, Henry Schneiderman,...
Training datasets for learning of object categories are often contaminated or imperfect. We explore an approach to automatically identify examples that are noisy or troublesome fo...
Anelia Angelova, Yaser S. Abu-Mostafa, Pietro Pero...
We optimize over the set of corrected laplacians (CL) associated with a weighted graph to improve the average case normalized cut (NCut) of a graph. Unlike edge-relaxation SDPs, o...
In this paper, an optimization based learning method is proposed for image retrieval from graph model point of view. Firstly, image retrieval is formulated as a regularized optimi...