\relax \citation{Gentner82} \citation{Yao11} \citation{Fellbaum98} \citation{Deng09} \citation{Gentner82} \citation{Yao11} \@writefile{toc}{\contentsline {title}{Lecture Notes in Computer Science}{1}} \@writefile{toc}{\authcount {1}} \@writefile{toc}{\contentsline {author}{Authors' Instructions}{1}} \@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{1}} \newlabel{sec:intro}{{1}{1}} \@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Human actions lie in a continuous space, where the human pose can change continuously and convey different meanings.}}{1}} \newlabel{fig:continuous_space}{{1}{1}} \@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Human actions are highly related to human poses and objects. In the left two images, ``riding bike'' and ``riding horse'' are functionally related and the humans have similar poses. In the right two images, both humans are ``playing tennis''. The poses of the two humans are very different, but they both interact with the same object (tennis racket).}}{2}} \newlabel{fig:pose_demo}{{2}{2}} \@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces The human actions can be different even when the humans interact with the same object. We observe that human poses in ``riding bike'' are usually similar, whereas the poses in ``fixing a bike'' are very different.}}{2}} \newlabel{fig:bike}{{3}{2}} \citation{Clauset04,Flake02} \citation{Yao11} \citation{Bourdev09} \citation{Bourdev09} \@writefile{toc}{\contentsline {section}{\numberline {2}Prior Work}{3}} \@writefile{toc}{\contentsline {section}{\numberline {3}Algorithm}{3}} \@writefile{toc}{\contentsline {subsection}{\numberline {3.1}Action Images Represented by Poselets and Objects}{3}} \newlabel{sec:image}{{3.1}{3}} \citation{Yao11} \@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces Examples of poselets. Images in each row correspond to one poselet.}}{4}} \newlabel{fig:poselet}{{4}{4}} \@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces Demonstration of our annotation. The magenta bounding box indicates the object, while the red dots indicate the key points in the human body. The key points we consider are: top and bottom points of head and torso; top, bottom points and elbow of left and right arms; top, bottom, and knee of legs.}}{5}} \newlabel{fig:annotation}{{5}{5}} \@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces The left-most figure illustrates our division of the image locations with respect to the human. The right two images show the location of the same object in different actions.}}{5}} \newlabel{fig:object_location}{{6}{5}} \@writefile{toc}{\contentsline {subsection}{\numberline {3.2}The Stanford-40 Dataset and Action Network}{5}} \citation{Girvan02} \@writefile{toc}{\contentsline {subsection}{\numberline {3.3}Action Similarity and ``Action Community'' Detection}{6}} \@writefile{toc}{\contentsline {section}{\numberline {4}Results and Findings}{6}} \@writefile{lof}{\contentsline {figure}{\numberline {7}{\ignorespaces The distance between pairs of actions. Blue color indicates small distance, while red color indicates large distance. We set the distance between the same action class to be 0.}}{7}} \newlabel{fig:distance}{{7}{7}} \bibcite{Activity10}{1} \bibcite{Bourdev09}{2} \bibcite{Clauset04}{3} \bibcite{Deng09}{4} \bibcite{Fellbaum98}{5} \bibcite{Flake02}{6} \bibcite{Gentner82}{7} \@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces The action clustering results. Each row is a cluster, each column is the number of images of a specific action in different classes. Red color indicates large number, while blue color indicates small number.}}{8}} \newlabel{fig:cluster}{{8}{8}} \bibcite{Girvan02}{8} \bibcite{Yao11}{9}