{"search_session":{},"preferences":{"l":"fr","queryLanguage":"fr"},"patentId":"US_2003_0103565_A1","frontPageModel":{"patentViewModel":{"ref":{"entityRefId":"118-992-345-563-815","entityRefType":"PATENT"},"entityMetadata":{"linkedIds":{"empty":true},"tags":[],"collections":[{"id":8759,"type":"PATENT","title":"University of Columbia","description":"","access":"OPEN_ACCESS","displayAvatar":true,"attested":false,"itemCount":13487,"tags":[],"user":{"id":91044780,"username":"Cambialens","firstName":"","lastName":"","created":"2015-05-04T00:55:26.000Z","displayName":"Cambialens","preferences":"{\"usage\":\"public\",\"beta\":false}","accountType":"PERSONAL","isOauthOnly":false},"notes":[{"id":8203,"type":"COLLECTION","user":{"id":91044780,"username":"Cambialens","firstName":"","lastName":"","created":"2015-05-04T00:55:26.000Z","displayName":"Cambialens","preferences":"{\"usage\":\"public\",\"beta\":false}","accountType":"PERSONAL","isOauthOnly":false},"text":"
Searched applicants and Owners= \"Columbia Univ\", \"Univ Columbia\", \" Univ Colum*\", \"Colum* univ\", \"Univ* Colum* NOT British\".
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Searched applicants and Owners= \"Columbia Univ\", \"Univ Columbia\", \" Univ Colum*\", \"Colum* univ\", \"Univ* Colum* NOT British\".
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Total patents: more than 10k
extracting a set of domain specific features from fixed-length sliding windows of frames of the continuous compressed video;\n
determining a set of maximum likelihoods for each set of domain specific features using a plurality of sets of trained hidden Markov models; and\n
applying dynamic programming to each set of maximum likelihoods to determine a specific state for each fixed-length sliding window of frames of the continuous compressed video."],"number":1,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 1 wherein the extracting further comprises: \n
determining a dominant color ratio from each frame; and\n
determining an average motion intensity from each frame."],"number":2,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 2 wherein the dominant color ratio is \n4ηc=|Pd||P|,\nwhere P is a set of all pixels in each frame, and Pd is a set of pixels with a dominant color in each frame."],"number":3,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 2 wherein the average motion intensity is \n5m=1|Φ|∑Φvx2+vy2,\nwhere Φ represents a number of macro-blocks in each frame, and {overscore (υ)}=[υx, υy] is a motion vector for each macro-block."],"number":4,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 1 wherein a length of the window is in the range of one to five seconds."],"number":5,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 1 wherein the window slides forward in one second steps."],"number":6,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 1 further comprising: \n
smoothing the set of domain specific features with a temporal low-pass filter; and\n
normalizing the set of domain specific features with regard to a mean and variance of the entire set of domain specific features."],"number":7,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 1 wherein the plurality of sets of hidden Markov models are trained with a training video having frames with known states."],"number":8,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 1 wherein each set includes six hidden Markov models."],"number":9,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 1 wherein the states are P and B, and the sets of hidden Markov models are Ω{circumflex over (=)}ΩP∪ΩB={P1 . . . Pn;B1 . . . Bn}."],"number":10,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 10 wherein the set of maximum likelihood for each set of domain specific features is QP(t)=max {QPi(t)}, QB(t)=max {QNi(t)}, i=1, . . . , 6."],"number":11,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 1 wherein the domain specific features are modeled as a mixture of Gaussian distributions."],"number":12,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 1 wherein each set of the maximum likelihoods form a trellis grid, and the specific state corresponds to an optimal path through the lattice grid."],"number":13,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 13 wherein the trellis grid corresponds to states of the sets of hidden Markov models and state transitions of the hidden Markov models."],"number":14,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 1 further comprising: \n
segmenting the continuous compressed video according to the specific states."],"number":15,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 1 wherein the continuous compressed video is of a sporting event, and a dominant color ratio for each frame is determined from a color of a playing field, and an average motion intensity is determined from motion vectors of macro blocks of each frame."],"number":16,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 16 wherein the sporting event is a soccer game, and the color is green."],"number":17,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 16 wherein the states are play and break."],"number":18,"annotation":false,"claim":true,"title":false},{"lines":["The method of claim 10 wherein the continuous compressed video is of a soccer game, and a dominant color ratio for each frame is determined from a green color of a playing field, and an average motion intensity is determined from motion vectors of macro blocks of each frame, and the states P and B are play and break in the soccer game."],"number":19,"annotation":false,"claim":true,"title":false}]}},"filters":{"npl":[],"notNpl":[],"applicant":[],"notApplicant":[],"inventor":[],"notInventor":[],"owner":[],"notOwner":[],"tags":[],"dates":[],"types":[],"notTypes":[],"j":[],"notJ":[],"fj":[],"notFj":[],"classIpcr":[],"notClassIpcr":[],"classNat":[],"notClassNat":[],"classCpc":[],"notClassCpc":[],"so":[],"notSo":[],"sat":[]},"sequenceFilters":{"s":"SEQIDNO","d":"ASCENDING","p":0,"n":10,"sp":[],"si":[],"len":[],"t":[],"loc":[]}}