Modeling guidance and recognition in categorical search: bridging human and computer object detection.

Search is commonly described as a repeating cycle of guidance to target-like objects, followed by the recognition of these objects as targets or distractors. Are these indeed separate processes using different visual features? We addressed this question by comparing observer behavior to that of support vector machine (SVM) models trained on guidance and recognition tasks. Observers searched for a categorically defined teddy bear target in four-object arrays.

Multi-voxel pattern analysis of selective representation of visual working memory in ventral temporal and occipital regions.

While previous results from univariate analysis showed that the activity level of the parahippocampal gyrus (PHG) but not the fusiform gyrus (FG) reflects selective maintenance of the cued picture category, present results from multi-voxel pattern analysis (MVPA) showed that the spatial response patterns of both regions can be used to differentiate the selected picture category in working memory. The ventral temporal and occipital areas including the PHG and FG have been shown to be specialized in perceiving and processing different kinds of visual information, though their role in the representation of visual working memory remains unclear. To test whether the PHG and FG show spatial response patterns that reflect selective maintenance of task-relevant visual working memory in comparison with other posterior association regions, we reanalyzed data from a previous fMRI study of visual working memory with a cue inserted during the delay period of a delayed recognition task.

Babytalk: understanding and generating simple image descriptions.

We present a system to automatically generate natural language descriptions from images. This system consists of two parts. The first part, content planning, smooths the output of computer vision-based detection and recognition algorithms with statistics mined from large pools of visually descriptive text to determine the best content words to use to describe an image.

Efficient classification for additive kernel SVMs.

We show that a class of nonlinear kernel SVMs admits approximate classifiers with runtime and memory complexity that is independent of the number of support vectors. This class of kernels, which we refer to as additive kernels, includes widely used kernels for histogram-based image comparison like intersection and chi-squared kernels. Additive kernel SVMs can offer significant improvements in accuracy over linear SVMs on a wide variety of tasks while having the same runtime, making them practical for large-scale recognition or real-time detection tasks.

Describable Visual Attributes for Face Verification and Image Search.

We introduce the use of describable visual attributes for face verification and image search. Describable visual attributes are labels that can be given to an image to describe its appearance. This paper focuses on images of faces and the attributes used to describe them, although the concepts also apply to other domains.