Multiple-instance content-based image retrieval employing isometric embedded similarity measure

In image-based retrieval, global or local features sufficiently discriminative to summarize the image content are commonly extracted first. Traditional features, such as color, texture, shape or corner, characterizing image content are not reliable in terms of similarity measure. A good match in the feature domain does not necessarily map to image pairs with similar relationship. Applying these features as search keys may retrieve dissimilar false-positive images, or leave similar false-negative ones behind. Moreover, images are inherently ambiguous since they contain a great amount of information that justifies many different facets of interpretation. Using a single image to query a database might employ features that do not match user's expectation and retrieve results with low precision/recall ratios. How to automatically extract reliable image features as a query key that matches user's expectation in a content-based image retrieval (CBIR) system is an important topic. The objective of the present work is to propose a multiple-instance learning image retrieval system by incorporating an isometric embedded similarity measure. Multiple-instance learning is a way of modeling ambiguity in supervised learning given multiple examples. From a small collection of positive and negative example images, semantically relevant concepts can be derived automatically and employed to retrieve images from an image database. Each positive and negative example images are represented by a linear combination of fractal orthonormal basis vectors. The mapping coefficients of an image projected onto each orthonormal basis constitute a feature vector. The Euclidean-distance similarity measure is proved to remain consistent, i.e., isometric embedded, between any image pairs before and after the projection onto orthonormal axes. Not only similar images generate points close to each other in the feature space, but also dissimilar ones produce feature points far apart. The utilization of an isometric-embedded fractal-based technique to extract reliable image features, combined with a multiple-instance learning paradigm to derive relevant concepts, can produce desirable retrieval results that better match user's expectation. In order to demonstrate the feasibility of the proposed approach, two sets of test for querying an image database are performed, namely, the fractal-based feature extraction algorithm vs. three other feature extractors, and single-instance vs. multiple-instance learning. Both the retrieval results, execution time and precision/recall curves show favorably for the proposed multiple-instance fractal-based approach.