Multi-objective optimization (MOO) aims to optimize multiple, possibly conflicting objectives with widespread applications. We introduce a novel interacting particle method for MOO inspired by molecular dynamics simulations. Our approach combines overdamped Langevin and birth-death dynamics, incorporating a “dominance potential” to steer particles toward global Pareto optimality. In contrast to previous methods, our method is able to relocate dominated particles, making it particularly adept at managing Pareto fronts of complicated geometries. Our method is also theoretically grounded as a Wasserstein-Fisher-Rao gradient flow with convergence guarantees. Extensive experiments confirm that our approach outperforms state-of-the-art methods on challenging synthetic and real-world datasets.

In this paper we propose a novel classification based framework for finding a small number of images that summarize a given concept. Our method exploits metadata information available with the images to get category information using Latent Dirichlet Allocation. Using this category information for each image, we solve the underlying classification problem by building a sparse classifier model for each concept. We demonstrate that the images that specify the sparse model form a good summary. In particular, our summary satisfies important properties such as likelihood, diversity and balance in both visual and semantic sense. Furthermore, the framework allows users to specify desired distributions over categories to create personalized summaries. Experimental results on seven broad query types show that the proposed method performs better than state-of-the-art methods.

With the explosion of information on any topic, the need for ranking is becoming very critical. Ranking typically depends on several aspects. Products, for example, have several aspects like price, recency, rating, etc. Product ranking has to bring the "best" product which is recent and highly rated. Hence ranking has to satisfy multiple objectives. In this paper, we explore multi-objective ranking of comments using Hodge decomposition. While Hodge decomposition produces a globally consistent ranking, a globally inconsistent component is also present. We propose an active learning strategy for the reduction of this component. Finally, we develop techniques for online Hodge decomposition. We experimentally validate the ideas presented in this paper.

Pornographic image detection is an important and challenging problem. Detection of pornography on the Internet is even more challenging because of the scale (billions of images) and diversity (small to very large images, graphic, grey scale images, etc.) of image content. The performance requirements (precision, recall, and speed) are also very stringent. Because of this, no single technique provides the required performance. In this paper, we describe a framework for detecting images with pornographic content. The framework combines various techniques based on object-level and pixel-level analysis of image content. To enable high-precision, we detect body parts (including faces) in images. For high-recall, low-level techniques like color and texture features are used. For adaptation to new datasets, we also support learning of appropriate color models from weakly-labeled datasets. In addition to image-based analysis, both text-based and site-level analysis are performed. Unlike many adult detection techniques, we explicitly leverage techniques like texture analysis and face detection for non-adult content identification. The multiple cues are combined in a systematic manner using ROC analysis and boosting. Evaluations on real world web data indicate that the system has the best performance among the systems compared.

We present I-FAC, a novel fuzzy associative classification algorithm for object class detection in images using interest points. In object class detection, the negative class CNis generally vague (CN= U - CP; where U and CPare the universal and positive classes respectively). But, image classification necessarily requires both positive and negative classes for training. I-FAC is a single class image classifier that relies only on the positive class for training. Because of its fuzzy nature, I-FAC also handles polysemy and synonymy (common problems in most crisp (non-fuzzy) image classifiers) very well. As associative classification leverages frequent patterns mined from a given dataset, its performance as adjudged from its false-positive-rate(FPR)-versus-recall curve is very good, especially at lower FPRs when its recall is even better. IFAC has the added advantage that the rules used for classification have clear semantics, and can be comprehended easily, unlike other classifiers, such as SVM, which act as black-boxes. From an empirical perspective (on standard public datasets), the performance of I-FAC is much better, especially at lower FPRs, than that of either bag-of-words (BOW) or SVM (both using interest points).

The dominant advertising model on the Internet is based on matching search keywords or web page content to ads. The matching is based on text content. There is an explosion of media content on the Internet. Matching based on image content has not taken off on the Internet despite the huge popularity of sites like flickr.com. In this demo, we show we can adapt techniques from image matching to enable a logo-based advertisement matching system for photo sharing sites like Flickr. Logo detection is based on detection and matching of salient points.

Abstract In this paper we present a framework for designing quality walkthroughs. Our framework aids the content creator in designing tours where one sees interesting objects for the most part of the tour. Further, the speed of the tour is controlled by what is shown. In particular we consider the designing of walkthroughs of scenes constructed by image‐based techniques where such a design framework helps to optimize on the effort needed to create the content. Copyright © 2007 John Wiley & Sons, Ltd.

There is an explosion of multimedia content, especially image, on the Internet. Images as pixel arrays suffer from two deficiencies: semantic gap and geometric gap. There have been several attempts like object detection for closing the semantic gap. Recently the notion of community tagging of objects in an image has attracted a lot of attention. There has been lot of research in closing the geometric gap by 3D reconstruction. In this paper, we present 3D Buddy (3dB) -a system for geometrically tagging images for 3D reconstruction.