scispace - formally typeset
Open AccessJournal ArticleDOI

Object and scene recognition in tiny images

Reads0
Chats0
About
This article is published in Journal of Vision.The article was published on 2010-03-18 and is currently open access. It has received 14 citations till now. The article focuses on the topics: 3D single-object recognition & Object (computer science).

read more

Citations
More filters
Journal ArticleDOI

80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition

TL;DR: For certain classes that are particularly prevalent in the dataset, such as people, this work is able to demonstrate a recognition performance comparable to class-specific Viola-Jones style detectors.

80 million tiny images : a large dataset for non-parametric object and scene recognition

TL;DR: In this paper, a large dataset of 79,302,017 images collected from the Internet is used to explore the visual world with the aid of a variety of non-parametric methods.
Proceedings ArticleDOI

IM2GPS: estimating geographic information from a single image

TL;DR: This paper proposes a simple algorithm for estimating a distribution over geographic locations from a single image using a purely data-driven scene matching approach and shows that geolocation estimates can provide the basis for numerous other image understanding tasks such as population density estimation, land cover estimation or urban/rural classification.
Journal ArticleDOI

Annotating Images by Mining Image Search Results

TL;DR: This paper proposes a novel attempt at model-free image annotation, which is a data-driven approach that annotates images by mining their search results, and enables annotating with unlimited vocabulary and is highly scalable and robust to outliers.
Proceedings ArticleDOI

Recognition by association via learning per-exemplar distances

TL;DR: This work uses the distance functions to detect and segment objects in novel images by associating the bottom-up segments obtained from multiple image segmentations with the exemplar regions and learns separate distance functions for each exemplar.