Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR)

A method for improving the evaluation of the similarity of images based upon image features, the method comprising calculating a weighted similarity distance between a first image and a set of images as a distance between them in a feature space, wherein the calculating employs this evaluation:

• Dis ⁢ ( j ) = ∑ i = 0 N R ⁢ ⁢ w i * ( 1 - Sim ⁢ ( j , X i + ) ) ;
• where
• j is the first image;
• NR is a number of images in the set of images;
• Xi+, i=1, . . . NR is defined as the ith image;
• wi is the normalized weight of the images in the set of images.

A method for improving the evaluation of the similarity of images based upon image features, the method comprising defining a subject image to be similar to a set of example images because it is within a minimal distance of the set of example images within a feature space, wherein the distance between the subject image and the set of example images is calculated based upon this evaluation:

• Dis ⁢ ( j ) = ∑ i = 0 N R ⁢ ⁢ w i * ( 1 - Sim ⁢ ( j , X i + ) ) ;
• where
• j is the subject image;
• NR is a number of images in the set of example images;
• Xi+, i=1, . . . NR is defined as the ith image;
• wi is the normalized weight of the images in the set of example images.

A system for improving iterative results of Content-Based Image Retrieval (CBIR) using relevance feedback, the system comprising:

• a relevance feedback sub-system configured to obtain a set of positive feedback images and a set of negative feedback images via relevance feedback, the set of positive feedback images are those images deemed semantically relevant and the set of negative feedback images are those deemed semantically less relevant;
• a feedback analyzer configured, within a feature space, to:
• move a positive candidate images towards the set of positive feedback images by adjusting distance metrics of the positive candidate image, the positive candidate image having similar low-level features as those of the set of positive feedback images, wherein the adjusting of distance metric of the positive candidate image employs this evaluation:
• Dis ⁢ ( j ) = ∑ i = 0 N R ⁢ ⁢ w i * ( 1 - Sim ⁢ ( j , X i + ) ) ;
• where
• j is the positive candidate image;
• NR is a number of images in the set of positive feedback images;
• Xi+, i=1, . . . NR is defined as the ith image;
• wi is the normalized weight of the images in the set of positive feedback images;
• distance a negative candidate image from the set of positive feedback images by adjusting distance metrics of the negative candidate images, the negative candidate image having similar low-level features as those of the set of negative feedback images.

A system for improving iterative results of Content-Based Image Retrieval (CBIR) using relevance feedback, the system comprising:

• a memory comprising a set of computer program instructions;
• and
• a processor coupled to the memory, the processor being configured to execute the computer program instructions, which comprise:
• obtaining a set of positive feedback images and a set of negative feedback images via relevance feedback, the set of positive feedback images are those images deemed semantically relevant and the set of negative feedback images are those deemed semantically less relevant;
• within a feature space, moving a positive candidate image towards the set of positive feedback images by adjusting distance metrics of the positive candidate image, the positive candidate image having similar low-level features as those of the set of positive feedback images, wherein the adjusting of distance metrics of the positive candidate image employs this evaluation:
• Dis ⁢ ( j ) = ∑ i = 0 N R ⁢ ⁢ w i * ( 1 - Sim ⁢ ( j , X i + ) ) ;
• where
• j is the positive candidate image;
• NR is a number of images in the set of positive feedback images;
• Xi+, i=1, . . . NR is defined as the ith image;
• wi is the normalized weight of the images in the set of positive feedback images;
• within a feature space, distancing a negative candidate image from the set of positive feedback images by adjusting distance metrics of the negative candidate image, the negative candidate images having similar low-level features as those of the set of negative feedback images.