Chained ensemble classifier for image annotation

Abstract

Image annotation is the task of assigning keywords or identifiers to images, holistically or in specific regions. These keywords serve as descriptors of high-level semantics to facilitate retrieval and organization of visual information. It plays an important role in content-based image understanding, as well as in areas such as object recognition in robotics, content-based image searching and knowledge extraction. Automatic image annotation is usually approached by means of supervised classification, where a set of previously annotated images is required to train a learning algorithm that later predicts the labels for new images. This paper proposes a novel ensemble classifier for the supervised image annotation task inspired in chain classifiers. In the proposed approach a chain of individual classifiers is build, where each classifier is trained by using a different modality. In addition, the input space of models in the chain is augmented with the output of the preceding model in the sequence. Each model in the chain deals with the same classification problem, making the proposed method an ensemble model build from multimodal data. To the best of our knowledge, chain classifiers have not been used in this particular setting. Experimental results in a challenging image collection show that the proposed method is able to obtain an f −value superior to 0.5, outperforming related work.