Bagofvisualwords Model for Image Classification Based on Spatial Semantic Distribution To satisfy the requirement of image classification in the application of image retrieval, a novel method of image representation based on bagofvisualwords model is proposed in the paper to describe the spatial semantic distribution of associated features. Firstly, the extracted SIFT features are mapped into visual words including certain semantic information. According to spatial pyramid hierarchy, the specific region is divided with local features, and the spatial distribution of associated features is analyzed from different aspects and in various regions. In this way, the semantic phrases are established with local features. Next, the spatial semantic lexicon is constructed with sparse encoding of spatial semantic phrases, and the images are described with the form of sparse statistical histogram vectors. Finally, the vectors of images are classified with the classifier embedded with the improved bagofvisualwords model. The experimental results show that the accuracy of image classification is significantly enhanced which is benefited from the Bagofvisualwords model with spatial semantic distribution.

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.