Statistical-learning-based object detection is an important topic in computer vision. It learns
visual representation from annotated exemplars to identify semantic defined objects in …

Color naming, which relates colors with color names, can help people with a semantic
analysis of images in many computer vision applications. In this paper, we propose a novel …

Hundreds of thousands of photographs are uploaded to the internet every minute through
various social networking and photo sharing platforms. While some images get millions of …

This paper starts from the observation that multiple top performing pedestrian detectors can
be modelled by using an intermediate layer filtering low-level features in combination with a …

Street-level imagery has covered the comprehensive landscape of urban areas. Compared
to satellite imagery, this new source of image data has the advantage in fine-grained …

We have developed a technique to study how good computers can be at diagnosing
gastrointestinal lesions from regular (white light and narrow banded) colonoscopic videos …

Rapid and reliable identification of insects is important in many contexts, from the detection
of disease vectors and invasive species to the sorting of material from biodiversity …

The objective of this paper is the effective transfer of the Convolutional Neural Network
(CNN) feature in image search and classification. Systematically, we study three facts in …

The aim of this paper is fine-grained categorization without human interaction. Different from
prior work, which relies on detectors for specific object parts, we propose to localize …

Logging food and calorie intake has been shown to facilitate weight management.
Unfortunately, current food logging methods are time-consuming and cumbersome, which …