Reliable estimation of visual saliency allows appropriate processing of images without prior knowledge of their contents, and thus remains an important step in many computer vision tasks including image segmentation, object recognition, and adaptive compression. We propose a regional contrast based saliency extraction algorithm, which simultaneously evaluates global contrast differences and spatial coherence. The proposed algorithm is simple, efficient, and yields full resolution saliency maps. Our algorithm consistently outperformed existing saliency detection methods, yielding higher precision and better recall rates, when evaluated using one of the largest publicly available data sets. We also demonstrate how the extracted saliency map can be used to create high quality segmentation masks for subsequent image processing. 1.

Training a generic objectness measure to produce a small set of candidate object windows, has been shown to speed up the classical sliding window object detection paradigm. We observe that generic objects with well-defined closed boundary can be discriminated by looking at the norm of gradients, with a suitable resizing of their cor-responding image windows in to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gra-dients as a simple 64D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this fea-ture, namely binarized normed gradients (BING), can be used for efficient objectness estimation, which requires only a few atomic operations (e.g. ADD, BITWISE SHIFT, etc.). Experiments on the challenging PASCAL VOC 2007 dataset show that our method efficiently (300fps on a single lap-top CPU) generates a small set of category-independent, high quality object windows, yielding 96.2 % object detec-tion rate (DR) with 1,000 proposals. Increasing the num-bers of proposals and color spaces for computing BING fea-tures, our performance can be further improved to 99.5% DR. 1.

The concepts of objects and attributes are both impor-tant for describing images precisely, since verbal descrip-tions often contain both adjectives and nouns (e.g. ‘I see a shiny red chair’). In this paper, we formulate the prob-lem of joint visual attribute and object class image seg-mentation as a dense multi-labelling problem, where each pixel in an image can be associated with both an object-class and a set of visual attributes labels. In order to learn the label correlations, we adopt a boosting-based piecewise training approach with respect to the visual appearance and co-occurrence cues. We use a filtering-based mean-field approximation approach for efficient joint inference. Further, we develop a hierarchical model to incorporate region-level object and attribute information. Experiments on the aPASCAL, CORE and attribute augmented NYU in-door scenes datasets show that the proposed approach is able to achieve state-of-the-art results. 1.

more “winter ” more “night” more “warm” more “moist” more “rain”more “autumn” Figure 1: Our method enables high-level editing of outdoor photographs. In this example, the user provides an input image (left) and six attribute queries corresponding to the desired changes, such as more “autumn”. Our method hallucinates six plausible versions of the scene with the desired attributes (right), by learning local color transforms from a large dataset of annotated outdoor webcams. We live in a dynamic visual world where the appearance of scenes changes dramatically from hour to hour or season to season. In this work we study “transient scene attributes ” – high level prop-erties which affect scene appearance, such as “snow”, “autumn”, “dusk”, “fog”. We define 40 transient attributes and use crowd-sourcing to annotate thousands of images from 101 webcams. We use this “transient attribute database ” to train regressors that can predict the presence of attributes in novel images. We demonstrate a photo organization method based on predicted attributes. Finally we propose a high-level image editing method which allows a user to adjust the attributes of a scene, e.g. change a scene to be “snowy” or “sunset”. To support attribute manipulation we introduce a novel appearance transfer technique which is simple and fast yet compet-itive with the state-of-the-art. We show that we can convincingly modify many transient attributes in outdoor scenes.

We present a new interactive and online approach to 3D scene understand-ing. Our system, SemanticPaint, allows users to simultaneously scan their environment, whilst interactively segmenting the scene simply by reaching out and touching any desired object or surface. Our system continuously learns from these segmentations, and labels new unseen parts of the envi-ronment. Unlike offline systems, where capture, labeling and batch learning often takes hours or even days to perform, our approach is fully online. This provides users with continuous live feedback of the recognition during capture, allowing them to immediately correct errors in the segmentation and/or learning – a feature that has so far been unavailable to batch and offline methods. This leads to models that are tailored or personalized specif-ically to the user’s environments and object classes of interest, opening up the potential for new applications in augmented reality, interior design, and human/robot navigation. It also provides the ability to capture substantial labeled 3D datasets for training large-scale visual recognition systems.