CiteULike: xiangang library [7 articles]

A Comparative Study on Feature Selection in Text Categorization

Yiming Yang — 2005-10-18T16:09:58-00:00

(1997), pp. 412-420.

Discriminative training for object recognition using image patches

T Deselaers — 2007-09-23T13:51:05-00:00

Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, Vol. 2 (2005), pp. 157-162 vol. 2.

We present a method for automatically learning discriminative image patches for the recognition of given object classes. The approach applies discriminative training of log-linear models to image patch histograms. We show that it works well on three tasks and performs significantly better than other methods using the same features. For example, the method decides that patches containing an eye are most important for distinguishing face from background images. The recognition performance is very competitive with error rates presented in other publications. In particular, a new best error rate for the Caltech motorbikes data of 1.5% is achieved.

Pfinder: real-time tracking of the human body

CR Wren — 2006-08-15T19:39:45-00:00

Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 19, No. 7. (1997), pp. 780-785.

Pfinder is a real-time system for tracking people and interpreting their behavior. It runs at 10 Hz on a standard SGI Indy computer, and has performed reliably on thousands of people in many different physical locations. The system uses a multiclass statistical model of color and shape to obtain a 2D representation of head and hands in a wide range of viewing conditions. Pfinder has been successfully used in a wide range of applications including wireless interfaces, video databases, and low-bandwidth coding

Near-Duplicate Keyframe Identification With Interest Point Matching and Pattern Learning

WL Zhao — 2007-09-10T04:30:07-00:00

Multimedia, IEEE Transactions on, Vol. 9, No. 5. (2007), pp. 1037-1048.

<para> This paper proposes a new approach for near-duplicate keyframe (NDK) identification by matching, filtering and learning of local interest points (LIPs) with PCA-SIFT descriptors. The issues in matching reliability, filtering efficiency and learning flexibility are novelly exploited to delve into the potential of LIP-based retrieval and detection. In matching, we propose a one-to-one symmetric matching (OOS) algorithm which is found to be highly reliable for NDK identification, due to its capability in excluding false LIP matches compared with other matching strategies. For rapid filtering, we address two issues: speed efficiency and search effectiveness, to support OOS with a new index structure called LIP-IS. By exploring the properties of PCA-SIFT, the filtering capability and speed of LIP-IS are asymptotically estimated and compared to locality sensitive hashing (LSH). Owing to the robustness consideration, the matching of LIPs across keyframes forms vivid patterns that are utilized for discriminative learning and detection with support vector machines. Experimental results on TRECVID-2003 corpus show that our proposed approach outperforms other popular methods including the techniques with LSH in terms of retrieval and detection effectiveness. In addition, the proposed LIP-IS successfully speeds up OOS for more than ten times and possesses several avorable properties compared to LSH. </para>

Learning a Sparse Representation for Object Detection

S Agarwal — 2007-09-09T14:13:40-00:00

Computer Vision - ECCV 2002: 7th European Conference on Computer Vision, Copenhagen, Denmark, May 28-31, 2002. Proceedings, Part IV (2002), pp. 97-101.

We present an approach for learning to detect objects in still gray images, that is based on a sparse, part-based representation of objects. A vocabulary of information-rich object parts is automatically constructed from a set of sample images of the object class of interest. Images are then represented using parts from this vocabulary, along with spatial relations observed among them. Based on this representation, a feature-efficient learning algorithm is used to learn to detect instances of the object class. The framework developed can be applied to any object with distinguishable parts in a relatively fixed spatial configuration. We report experiments on images of side views of cars. Our experiments show that the method achieves high detection accuracy on a difficult test set of real-world images, and is highly robust to partial occlusion and background variation. In addition, we discuss and offer solutions to several methodological issues that are significant for the research community to be able to evaluate object detection approaches.

Class-Specific, Top-Down Segmentation

E Borenstein — 2007-09-09T13:23:53-00:00

Computer Vision - ECCV 2002 : 7th European Conference on Computer Vision, Copenhagen, Denmark, May 28-31, 2002. Proceedings, Part II (2002), pp. 639-641.

In this paper we present a novel class-based segmentation method, which is guided by a stored representation of the shape of objects within a general class (such as horse images). The approach is different from bottom-up segmentation methods that primarily use the continuity of grey-level, texture, and bounding contours. We show that the method leads to markedly improved segmentation results and can deal with significant variation in shape and varying backgrounds. We discuss the relative merits of class-specific and general image-based segmentation methods and suggest how they can be usefully combined. Keywords: Grouping and segmentation; Figure-ground; Top-down processing; Object classification

Hierarchical part-based visual object categorization

G Bouchard — 2007-09-08T05:58:21-00:00

Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, Vol. 1 (2005), pp. 710-715 vol. 1.

We propose a generative model that codes the geometry and appearance of generic visual object categories as a loose hierarchy of parts, with probabilistic spatial relations linking parts to subparts, soft assignment of subparts to parts, and scale invariant keypoint based local features at the lowest level of the hierarchy. The method is designed to efficiently handle categories containing hundreds of redundant local features, such as those returned by current key-point detectors. This robustness allows it to outperform constellation style models, despite their stronger spatial models. The model is initialized by robust bottom-up voting over location-scale pyramids, and optimized by expectation-maximization. Training is rapid, and objects do not need to be marked in the training images. Experiments on several popular datasets show the method's ability to capture complex natural object classes.