Tilke Judd

Bio: Tilke Judd is an academic researcher from Google. The author has contributed to research in topics: Salience (neuroscience) & Voting. The author has an hindex of 10, co-authored 23 publications receiving 3414 citations. Previous affiliations of Tilke Judd include Massachusetts Institute of Technology & Stanford University.

Learning to predict where humans look

Abstract: For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a scene Where eye tracking devices are not a viable option, models of saliency can be used to predict fixation locations Most saliency approaches are based on bottom-up computation that does not consider top-down image semantics and often does not match actual eye movements To address this problem, we collected eye tracking data of 15 viewers on 1003 images and use this database as training and testing examples to learn a model of saliency based on low, middle and high-level image features This large database of eye tracking data is publicly available with this paper

A Benchmark of Computational Models of Saliency to Predict Human Fixations

Abstract: Many computational models of visual attention have been created from a wide variety of different approaches to predict where people look in images. Each model is usually introduced by demonstrating performances on new images, and it is hard to make immediate comparisons between models. To alleviate this problem, we propose a benchmark data set containing 300 natural images with eye tracking data from 39 observers to compare model performances. We calculate the performance of 10 models at predicting ground truth fixations using three different metrics. We provide a way for people to submit new models for evaluation online. We find that the Judd et al. and Graph-based visual saliency models perform best. In general, models with blurrier maps and models that include a center bias perform well. We add and optimize a blur and center bias for each model and show improvements. We compare performances to baseline models of chance, center and human performance. We show that human performance increases with the number of humans to a limit. We analyze the similarity of different models using multidimensional scaling and explore the relationship between model performance and fixation consistency. Finally, we offer observations about how to improve saliency models in the future.

What Do Different Evaluation Metrics Tell Us About Saliency Models

Abstract: How best to evaluate a saliency model's ability to predict where humans look in images is an open research question. The choice of evaluation metric depends on how saliency is defined and how the ground truth is represented. Metrics differ in how they rank saliency models, and this results from how false positives and false negatives are treated, whether viewing biases are accounted for, whether spatial deviations are factored in, and how the saliency maps are pre-processed. In this paper, we provide an analysis of 8 different evaluation metrics and their properties. With the help of systematic experiments and visualizations of metric computations, we add interpretability to saliency scores and more transparency to the evaluation of saliency models. Building off the differences in metric properties and behaviors, we make recommendations for metric selections under specific assumptions and for specific applications.

Apparent ridges for line drawing

Abstract: Three-dimensional shape can be drawn using a variety of feature lines, but none of the current definitions alone seem to capture all visually-relevant lines. We introduce a new definition of feature lines based on two perceptual observations. First, human perception is sensitive to the variation of shading, and since shape perception is little affected by lighting and reflectance modification, we should focus on normal variation. Second, view-dependent lines better convey smooth surfaces. From this we define view-dependent curvature as the variation of the surface normal with respect to a viewing screen plane, and apparent ridges as the loci of points that maximize a view-dependent curvature. We present a formal definition of apparent ridges and an algorithm to render line drawings of 3D meshes. We show that our apparent ridges encompass or enhance aspects of several other feature lines.

CounterActive: an interactive cookbook for the kitchen counter

Abstract: We introduce CounterActive, an interactive kitchen cookbook that teaches people to cook. After describing the interactive system and the multimedia recipe schema, we discuss results of early user test and evaluation.

Global contrast based salient region detection

Abstract: Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information.

Learning to Detect a Salient Object

Abstract: In this paper, we study the salient object detection problem for images. We formulate this problem as a binary labeling task where we separate the salient object from the background. We propose a set of novel features, including multiscale contrast, center-surround histogram, and color spatial distribution, to describe a salient object locally, regionally, and globally. A conditional random field is learned to effectively combine these features for salient object detection. Further, we extend the proposed approach to detect a salient object from sequential images by introducing the dynamic salient features. We collected a large image database containing tens of thousands of carefully labeled images by multiple users and a video segment database, and conducted a set of experiments over them to demonstrate the effectiveness of the proposed approach.

Saliency Detection via Graph-Based Manifold Ranking

Abstract: Most existing bottom-up methods measure the foreground saliency of a pixel or region based on its contrast within a local context or the entire image, whereas a few methods focus on segmenting out background regions and thereby salient objects Instead of considering the contrast between the salient objects and their surrounding regions, we consider both foreground and background cues in a different way We rank the similarity of the image elements (pixels or regions) with foreground cues or background cues via graph-based manifold ranking The saliency of the image elements is defined based on their relevances to the given seeds or queries We represent the image as a close-loop graph with super pixels as nodes These nodes are ranked based on the similarity to background and foreground queries, based on affinity matrices Saliency detection is carried out in a two-stage scheme to extract background regions and foreground salient objects efficiently Experimental results on two large benchmark databases demonstrate the proposed method performs well when against the state-of-the-art methods in terms of accuracy and speed We also create a more difficult benchmark database containing 5,172 images to test the proposed saliency model and make this database publicly available with this paper for further studies in the saliency field

State-of-the-Art in Visual Attention Modeling

Abstract: Modeling visual attention-particularly stimulus-driven, saliency-based attention-has been a very active research area over the past 25 years. Many different models of attention are now available which, aside from lending theoretical contributions to other fields, have demonstrated successful applications in computer vision, mobile robotics, and cognitive systems. Here we review, from a computational perspective, the basic concepts of attention implemented in these models. We present a taxonomy of nearly 65 models, which provides a critical comparison of approaches, their capabilities, and shortcomings. In particular, 13 criteria derived from behavioral and computational studies are formulated for qualitative comparison of attention models. Furthermore, we address several challenging issues with models, including biological plausibility of the computations, correlation with eye movement datasets, bottom-up and top-down dissociation, and constructing meaningful performance measures. Finally, we highlight current research trends in attention modeling and provide insights for future.

Context-Aware Saliency Detection

Abstract: We propose a new type of saliency—context-aware saliency—which aims at detecting the image regions that represent the scene. This definition differs from previous definitions whose goal is to either identify fixation points or detect the dominant object. In accordance with our saliency definition, we present a detection algorithm which is based on four principles observed in the psychological literature. The benefits of the proposed approach are evaluated in two applications where the context of the dominant objects is just as essential as the objects themselves. In image retargeting, we demonstrate that using our saliency prevents distortions in the important regions. In summarization, we show that our saliency helps to produce compact, appealing, and informative summaries.