Abstract We study visual attention by detecting a salient object in an input image. We formulate salient object detection as an image segmentation problem, where we separate the salient object from the image background. We propose a set of novel features including multi-scale contrast

Recently locality-sensitive hashing (LSH) algorithms have attracted much attention owing to its empirical success and theoretic guarantee in large-scale visual search. In this paper we address the new topic of hashing with multi-label data, in which images in the database are assumed

Recently locality-sensitive hashing (LSH) algorithms have attracted much attention owing to its empirical success and theoretic guarantee in large-scale visual search. In this pa-per we address the new topic of hashing with multi-label data, in which images in the database are assumed

are typically obtained by combining Fisher Vectors (FV) and Spatial Pyramid Matching (SPM), leading to image signatures with dimensionality up to 262,144 [1]. This is a hindrance to large-scale image classification tasks, for which the attribute based approaches would be more efficient. This paper proposes a

we present a complete image retrieval system based on topic models and evaluate the suitability of different types of topic models for the task of large-scale retrieval on real-world databases. Different similarity measure are evaluated in a retrieval-by-example task. Next, we focus

and study the representation of images by topic models for content-based image retrieval. We focus on incorporating different types of visual descriptors into the topic modeling context. Three different fusion approaches are explored. The image representations for each fusion approach are learned

Bag of features (BoF) representation has attracted an increasing amount of attention in large scale image processing systems. BoF representation treats images as loose collections of local invariant descriptors extracted from them. The visual codebook is generally constructed by using

We present a novel method for the discovery and detection of visual object categories based on decompositions using topic models. The approach is capable of learning a compact and low dimensional representation for multiple visual categories from multiple view points without labeling

Abstract not found

and different fea-tures describe different aspects of the visual characteristics. Therefore, how to integrate heterogeneous visual features to do the semi-supervised learning is crucial for categorizing large-scale image data. In this paper, we propose a novel approach to integrate heterogeneous features