1. A method for facilitating semantic event classification of a group of image records related to an event, the method using an event detector system for providing: extracting a plurality of visual features from each of the image records; wherein the visual features include segmenting an image record into a number of regions, in which the visual features are extracted; generating a plurality of concept scores for each of the image records using the visual features, wherein each concept score corresponds to a visual concept and each concept score is indicative of a probability that the image record includes the visual concept; determining a pair-wise similarity between pairs of data points corresponding to training events to produce a codebook of semantic events by applying spectral clustering to group the data points into different clusters, with each cluster corresponding to one code word, based on the determined pair-wise similarities; mapping the training events to the codebook of semantic events to generate a first feature vector corresponding to each training event; training an event classifier based on the first feature vectors corresponding to the training events; generating a second feature vector by calculating a pairwise similarity between the concept scores of the image records and concept scores for predetermined training data points; mapping the second feature vectors to the codebook of semantic events to produce a third feature vector; and supplying the third feature vector to the event classifier that identifies at least one semantic event.
1. A method for facilitating semantic event classification of a group of image records related to an event, the method using an event detector system for providing: extracting a plurality of visual features from each of the image records; wherein the visual features include segmenting an image record into a number of regions, in which the visual features are extracted; generating a plurality of concept scores for each of the image records using the visual features, wherein each concept score corresponds to a visual concept and each concept score is indicative of a probability that the image record includes the visual concept; determining a pair-wise similarity between pairs of data points corresponding to training events to produce a codebook of semantic events by applying spectral clustering to group the data points into different clusters, with each cluster corresponding to one code word, based on the determined pair-wise similarities; mapping the training events to the codebook of semantic events to generate a first feature vector corresponding to each training event; training an event classifier based on the first feature vectors corresponding to the training events; generating a second feature vector by calculating a pairwise similarity between the concept scores of the image records and concept scores for predetermined training data points; mapping the second feature vectors to the codebook of semantic events to produce a third feature vector; and supplying the third feature vector to the event classifier that identifies at least one semantic event. 4. The method for facilitating semantic event classification as claimed in claim 1 , wherein the image records include at least one digital still image and at least one video segment.