Object classification is a crucial yet challenging vision ability to perfect The fundamental
objective is to educate computers to understand visuals the same way humans do. Due to …

In this paper we propose an approach to holistic scene understanding that reasons jointly
about regions, location, class and spatial extent of objects, presence of a class in the image …

The notion of using context information for solving high-level vision and medical image
segmentation problems has been increasingly realized in the field. However, how to learn …

This paper introduces an unsupervised color segmentation method. The underlying idea is
to segment the input image several times, each time focussing on a different salient part of …

Accurate segmentation and detection of all mixed and occluded objects in the complex
indoor/outdoor scenes become a vital topic of computer vision. These above-mentioned …

Object detection and recognition is an effective and fundamental technique used to track the
objects accurately in complex scenes. Over the last decade, object analysis has caught the …

Hierarchical conditional random fields have been successfully applied to object
segmentation. One reason is their ability to incorporate contextual information at different …

Online image hashing aims to update hash functions on-the-fly along with newly arriving
data streams, which has found broad applications in computer vision and beyond. To this …

Fast nearest neighbor search is becoming more and more crucial given the advent of large-
scale data in many computer vision applications. Hashing approaches provide both fast …

Abstract The Hierarchical Conditional Random Field (HCRF) model have been successfully
applied to a number of image labeling problems, including image segmentation. However …