Localized content based image retrieval

Rouhollah Rahmani, Sally A. Goldman, Hui Zhang, John Krettek, Jason E. Fritts
2005 Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval - MIR '05  
Classic Content-Based Image Retrieval (CBIR) takes a single non-annotated query image, and retrieves similar images from an image repository. Such a search must rely upon a holistic (or global) view of the image. Yet often the desired content of an image is not holistic, but is localized. Specifically, we define Localized Content-Based Image Retrieval as a CBIR task where the user is only interested in a portion of the image, and the rest of the image is irrelevant. Many classic CBIR systems
more » ... sic CBIR systems use relevance feedback to obtain images labeled as desirable or not desirable. Yet, these labeled images are typically used only to re-weight the features used within a global similarity measure. In this paper we present a localized CBIR system, Accio! , that uses labeled images in conjunction with a multiple-instance learning algorithm to first identify the desired object and re-weight the features, and then to rank images in the database using a similarity measure that is based upon individual regions within the image. We evaluate our system using a five-category natural scenes image repository, and benchmark data set, SIVAL, that we have constructed with 25 object categories.
doi:10.1145/1101826.1101863 dblp:conf/mir/RahmaniGZKF05 fatcat:r5n6smyvura47hel75trdecpva