Regionbased hidden markov models for image categorization and retrieval Hidden Markov models (HMMs) have been widely used in various fields, including image categorization and retrieval. Most of the existing methods train HMMs by lowlevel features of image blocks; however, the blockbased features can not reflect highlevel semantic concepts well. This paper proposes a new method to train HMMs by regionbased features, which can be obtained after image segmentation. Our work can be characterized by two key properties: (1) Regionbased HMM is adopted to achieve better categorization performance, for the regionbased features accord with the human perception better. (2) Multilayer semantic representation (MSR) is introduced to couple with regionbased HMM in a longterm relevance feedback framework for image retrieval. The experimental results demonstrate the effectiveness of our proposal in both aspects of categorization and retrieval.

Multiple Query ContentBased Image Retrieval Using Relevance Feature Weight Learning We propose a novel multiple query retrieval approach, named weightlearner, which relies on visual feature discrimination to estimate the distances between the query images and images in the database. For each query image, this discrimination consists of learning, in an unsupervised manner, the optimal relevance weight for each visual featuredescriptor. These feature relevance weights are designed to reduce the semantic gap between the extracted visual features and the userxe2x80x99s highlevel semantics. We mathematically formulate the proposed solution through the minimization of some objective functions. This optimization aims to produce optimal feature relevance weights with respect to the user query. The proposed approach is assessed using an image collection from the Corel database.