A method for content based image retrieval, the method comprising: selecting a query image; segmenting the selected image by applying a segmentation technique; extracting features from the segmented image by determining at least two feature descriptors, including a color feature descriptor and a texture feature descriptor; and determining, using the determined at least two feature descriptors of the segmented query image, a similarity of the query image to a plurality of images included in a database, which also include features extracted and computed by said at least two descriptors, the method being characterized in that the determined color feature descriptor and texture feature descriptor comprise global and local statistical measurements of at least the mean and variance of the numerical values for each of three color channels of each one of at least three different color spaces; and wherein the similarity of the query image to the plurality of images included in the database is determined using n-dimensional space metrics applied within a tailored hyper-cube cluster created in real-time, the hyper-cube cluster being defined by a vector with an m-dimensional radius, of a subset m of the total n-dimensions of the at least two feature descriptors together, with independent and different radius values for each dimension, wherein the hyper-cube cluster is centered on the space position of the query image and is based on an accepted percentage of the distance of a value of a query image position; and only performing 1:1 distance computations from the query image to all the items within the hyper-cube cluster. selecting a query image; segmenting the selected image by applying a segmentation technique; extracting features from the segmented image by determining at least two feature descriptors, including a color feature descriptor and a texture feature descriptor; and determining, using the determined at least two feature descriptors of the segmented query image, a similarity of the query image to a plurality of images included in a database, which also include features extracted and computed by said at least two descriptors, the method being characterized in that the determined color feature descriptor and texture feature descriptor comprise global and local statistical measurements of at least the mean and variance of the numerical values for each of three color channels of each one of at least three different color spaces; and wherein the similarity of the query image to the plurality of images included in the database is determined using n-dimensional space metrics applied within a tailored hyper-cube cluster created in real-time, the hyper-cube cluster being defined by a vector with an m-dimensional radius, of a subset m of the total n-dimensions of the at least two feature descriptors together, with independent and different radius values for each dimension, wherein the hyper-cube cluster is centered on the space position of the query image and is based on an accepted percentage of the distance of a value of a query image position; and only performing 1:1 distance computations from the query image to all the items within the hyper-cube cluster.