3. A method utilizing at least a computer processor for building a multimedia association graph comprising: collecting a set of multimedia documents; selecting at least one subset within the collected set of multimedia documents, the subset comprising at least one signal; creating a set of segments using a segmentation method, wherein the segmentation method segments the at least one signal into at least one level segment selected from a group consisting of: object level segments, attribute level segments and feature level segments; clustering the set of segments to create a cluster set; building a dictionary of multimedia attributes, wherein each attribute corresponds to a cluster in the cluster set; building a dictionary of classifiers comprising at least one entry, wherein each entry classifies an attribute in the dictionary of multimedia attributes, and wherein the dictionary of classifiers is built by a method comprising: computing a first descriptor f1 for at least a first signal in at least one cluster; computing a second descriptor f2 for at least a second signal in the the at least one cluster; building a first vector quantizer for space of the descriptor f1; building a second vector quantizer for space of the descriptor f2; creating a set of joint descriptors of size at least one, by combining the first and second vector quantitizers; computing a cluster document frequency (CDF) for at least one joint descriptor in the set of joint descriptors for the at least one cluster; computing a global document frequency (GDF) for the at least one joint descriptor in the set of joint descriptors; selecting a set of important joint descriptors from the set of joint descriptors based on the CDF and GDF; and computing a confidence score (CS) for the at least one cluster and for at least one important joint descriptor; and creating a graph wherein each node corresponds to an attribute in the multimedia attribute dictionary and a weight of edges or links indicates a degree of similarity between the attributes.
3. A method utilizing at least a computer processor for building a multimedia association graph comprising: collecting a set of multimedia documents; selecting at least one subset within the collected set of multimedia documents, the subset comprising at least one signal; creating a set of segments using a segmentation method, wherein the segmentation method segments the at least one signal into at least one level segment selected from a group consisting of: object level segments, attribute level segments and feature level segments; clustering the set of segments to create a cluster set; building a dictionary of multimedia attributes, wherein each attribute corresponds to a cluster in the cluster set; building a dictionary of classifiers comprising at least one entry, wherein each entry classifies an attribute in the dictionary of multimedia attributes, and wherein the dictionary of classifiers is built by a method comprising: computing a first descriptor f1 for at least a first signal in at least one cluster; computing a second descriptor f2 for at least a second signal in the the at least one cluster; building a first vector quantizer for space of the descriptor f1; building a second vector quantizer for space of the descriptor f2; creating a set of joint descriptors of size at least one, by combining the first and second vector quantitizers; computing a cluster document frequency (CDF) for at least one joint descriptor in the set of joint descriptors for the at least one cluster; computing a global document frequency (GDF) for the at least one joint descriptor in the set of joint descriptors; selecting a set of important joint descriptors from the set of joint descriptors based on the CDF and GDF; and computing a confidence score (CS) for the at least one cluster and for at least one important joint descriptor; and creating a graph wherein each node corresponds to an attribute in the multimedia attribute dictionary and a weight of edges or links indicates a degree of similarity between the attributes. 5. The method of claim 3 , wherein the confidence score is computed using the function CS ⁡ ( CDF , GDF ) = Z × log ⁡ ( 1 + CDF GDF ) where Z is any constant.