Results 1 - 10 of 3,058

SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries

by James Z. Wang, Jia Li, Gio Wiederhold - IEEE Transactions on Pattern Analysis and Machine Intelligence , 2001
"... The need for efficient content-based image retrieval has increased tremendously in many application areas such as biomedicine, military, commerce, education, and Web image classification and searching. We present here SIMPLIcity (Semanticssensitive Integrated Matching for Picture LIbraries), an imag ..."
Abstract - Cited by 551 (35 self) - Add to MetaCart
, which are characterized by color, texture, shape, and location. The system classifies images into semantic categories, such as textured-nontextured, graphphotograph. Potentially, the categorization enhances retrieval by permitting semantically-adaptive searching methods and narrowing down the searching

ClassView: Hierarchical Video Shot Classification, Indexing, and Accessing

by Jianping Fan, Ahmed K. Elmagarmid, Xingquan Zhu, Walid G. Aref, Lide Wu - IEEE TRANS. ON MULTIMEDIA , 2004
"... Recent advances in digital video compression and networks have made video more accessible than ever. However, the existing content-based video retrieval systems still suffer from the following problems. 1 ) Semantics---sensitive video classification problem because of the semantic gap between low-le ..."
Abstract - Cited by 42 (4 self) - Add to MetaCart
View, to make some advances toward more efficient video database indexing and access. 1) A hierarchical semantics-sensitive video classifier is proposed to shorten the semantic gap. The hierarchical tree structure of the semantics-sensitive video classifier is derived from the domain-dependent concept hierarchy

Learning realistic human actions from movies

by Ivan Laptev, Marcin Marszałek, Cordelia Schmid, Benjamin Rozenfeld - IN: CVPR. , 2008
"... The aim of this paper is to address recognition of natural human actions in diverse and realistic video settings. This challenging but important subject has mostly been ignored in the past due to several problems one of which is the lack of realistic and annotated video datasets. Our first contribut ..."
Abstract - Cited by 738 (48 self) - Add to MetaCart
contribution is to address this limitation and to investigate the use of movie scripts for automatic annotation of human actions in videos. We evaluate alternative methods for action retrieval from scripts and show benefits of a text-based classifier. Using the retrieved action samples for visual learning, we

Unsupervised learning of human action categories using spatial-temporal words

by Juan Carlos Niebles, Hongcheng Wang, Li Fei-fei - In Proc. BMVC , 2006
"... Imagine a video taken on a sunny beach, can a computer automatically tell what is happening in the scene? Can it identify different human activities in the video, such as water surfing, people walking and lying on the beach? To automatically classify or localize different actions in video sequences ..."
Abstract - Cited by 494 (8 self) - Add to MetaCart
Imagine a video taken on a sunny beach, can a computer automatically tell what is happening in the scene? Can it identify different human activities in the video, such as water surfing, people walking and lying on the beach? To automatically classify or localize different actions in video sequences

Recognizing action at a distance

by Alexei A. Efros, Alexander C. Berg, Greg Mori, Jitendra Malik - PROCEEDINGS OF THE IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION , 2003
"... Our goal is to recognize human actions at a distance, at resolutions where a whole person may be, say, 30 pixels tall. We introduce a novel motion descriptor based on optical flow measurements in a spatio-temporal volume for each stabilized human figure, and an associated similarity measure to be us ..."
Abstract - Cited by 504 (20 self) - Add to MetaCart
-temporal motion descriptor. To classify the action being performed by a human figure in a query sequence, we retrieve nearest neighbor(s) from a database of stored, annotated video sequences. We can also use these retrieved exemplars to transfer 2D/3D skeletons onto the figures in the query sequence, as well

A general framework for object detection

by Constantine P. Papageorgiou, Michael Oren, Tomaso Poggio - Sixth International Conference on , 1998
"... This paper presents a general trainable framework for object detection in static images of cluttered scenes. The detection technique we develop is based on a wavelet representation of an object class derived from a statistical analysis of the class instances. By learning an object class in terms of ..."
Abstract - Cited by 395 (21 self) - Add to MetaCart
of a subset of an overcomplete dictionary of wavelet basis functions, we derive a compact representation of an object class which is used as an input to a suppori vector machine classifier. This representation overcomes both the problem of in-class variability and provides a low false detection rate

Ensemble Tracking

by Shai Avidan - IEEE Transactions on Pattern Analysis and Machine Intelligence , 2007
"... We consider tracking as a binary classification problem, where an ensemble of weak classifiers is trained on-line to distinguish between the object and the background. The ensemble of weak classifiers is combined into a strong classifier using AdaBoost. The strong classifier is then used to label pi ..."
Abstract - Cited by 328 (2 self) - Add to MetaCart
We consider tracking as a binary classification problem, where an ensemble of weak classifiers is trained on-line to distinguish between the object and the background. The ensemble of weak classifiers is combined into a strong classifier using AdaBoost. The strong classifier is then used to label

Moving Target Classification and Tracking from Real-time Video

by Alan J. Lipton, Hironobu Fujiyoshi, Raju S. Patil , 1998
"... This paper describes an end-to-end method for extracting moving targets from a real-time video stream, classifying them into predefined categories according to imagebased properties, and then robustly tracking them. Moving targets are detected using the pixel wise difference between consecutive imag ..."
Abstract - Cited by 290 (6 self) - Add to MetaCart
This paper describes an end-to-end method for extracting moving targets from a real-time video stream, classifying them into predefined categories according to imagebased properties, and then robustly tracking them. Moving targets are detected using the pixel wise difference between consecutive

Comparison of Video Shot Boundary Detection Techniques

by John S. Boreczky, Lawrence A. Rowe , 1996
"... Many algorithms have been proposed for detecting video shot boundaries and classifying shot and shot transition types. Few published studies compare available algorithms, and those that do have looked at limited range of test material. This paper ..."
Abstract - Cited by 255 (4 self) - Add to MetaCart
Many algorithms have been proposed for detecting video shot boundaries and classifying shot and shot transition types. Few published studies compare available algorithms, and those that do have looked at limited range of test material. This paper

Image segmentation in video sequences: A probabilistic approach

by Nir Friedman, Stuart Russell , 1997
"... "Background subtraction" is an old technique for finding moving objects in a video sequence---for example, cars driving on a freeway. The idea is that subtracting the current image from a time-averaged background image will leave only nonstationary objects. It is, however, a crude approxim ..."
Abstract - Cited by 256 (0 self) - Add to MetaCart
"Background subtraction" is an old technique for finding moving objects in a video sequence---for example, cars driving on a freeway. The idea is that subtracting the current image from a time-averaged background image will leave only nonstationary objects. It is, however, a crude
Results 1 - 10 of 3,058