Image classification by search with explicitly and implicitly semantic representations

Image classification refers to the task of automatically classifying the categories of images based on the contents. This task is typically solved using visual features with the histogram based classification scheme. Although effective, this strategy has two drawbacks. On one hand, histogram based representation often disregards the object layout which is very important for classification. On the other hand, visual features are unable to fully separate different images due to the semantic gap. To solve these two problems, in this paper, we propose a novel image classification method by explicitly and implicitly representing the images with searching strategy. First, to make use of object layouts, we randomly select a number of regions and then use these regions for image representations. Second, we generate the explicitly semantic representations using a number of pre-learned semantic models. Third, we measure the visual similarities with the Internet images and use the text information for implicitly semantic representations. Since Internet images are contaminated with noise, the resulting representations only implicitly reflect the contents of images. Finally, both the explicitly and implicitly semantic representations are jointly modeled for image classifications by training bi-linear classifiers. We evaluate the effectiveness of the proposed image classification by search with explicitly and implicitly semantic representations method (EISR) on the Scene-15 dataset, the MIT-Indoor dataset, the UIUC-Sports dataset and the PASCAL VOC 2007 dataset. The experimental results prove the usefulness of the proposed method.