Ishani Chakraborty

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In this paper, we report a study that examines the relationship between image-based computational analyses of web pages and users' aesthetic judgments about the same image material. Web pages were iteratively decomposed into quadrants of minimum entropy (quadtree decomposition) based on low-level image statistics, to permit a characterization of these pages(More)
We present a unified framework for detecting and classifying people interactions in unconstrained user generated images. Unlike previous approaches that directly map people/face locations in 2D image space into features for classification, we first estimate camera viewpoint and people positions in 3D space and then extract spatial configuration features(More)
In this paper we propose an algorithm for contour-based object detection in cluttered images. Contour of an object shape is approximated as a set of line segments and object detection is framed as matching contour segments of an image (i.e.,an edge image) to a boundary model of an object (i.e., a line drawing). Local shape is abstracted as a group of(More)
We consider the problem of naming objects in complex, natural scenes containing widely varying object appearance and subtly different names. Informed by cognitive research, we propose an approach based on sharing context based object hypotheses between visual and lexical spaces. To this end, we present the Visual Semantic Integration Model (VSIM) that(More)
In this paper, we describe a topic-model based approach for object detection in complex and cluttered scenes. We first learn a generic object model and then refine it by including contextual knowledge. This two-step learning proceeds with minimal supervision and without negative examples. In the first step, a generic object detector identifies object from(More)
In this paper, we propose a part-based approach to localize objects in cluttered images. We represent object parts as boundary segments and image patches. A semi-local grouping of parts named superfeatures encodes appearance and connectivity within a neighborhood. To match parts, we integrate inter-feature similarities and intra-feature connectivity via a(More)
In this paper, we propose an entity centric region of interest detection and visual-semantic pooling scheme for complex event detection in YouTube-like videos. Our method is based on the hypothesis that many YouTube-like videos involve people interacting with each other and objects in their vicinity. Based on this hypothesis, we first discover an Area of(More)
In collaboration with Children's National Medical Center, Washington D.C. Trauma care workflow analysis through video based activity recognition. Identify key trauma procedures and represent these procedures using spatio-temporal, local and global features. Propose an activity grammar and model activities using a temporal Markov Logic Network. Correlating(More)