Data-Driven Detection of Prominent Objects.

This article deals with the detection of prominent objects in images. As opposed to the standard approaches based on sliding windows, we study a fundamentally different solution by formulating the supervised prediction of a bounding box as an image retrieval task. Indeed, given a global image descriptor, we find the most similar images in an annotated dataset, and transfer the object bounding boxes. We refer to this approach as data-driven detection (DDD). Our key novelty is to design or learn image similarities that explicitly optimize some aspect of the transfer unlike previous work which uses generic representations and unsupervised similarities. In a first variant, we explicitly learn to transfer, by adapting a metric learning approach to work with image and bounding box pairs. Second, we use a representation of images as object probability maps computed from low-level patch classifiers. Experiments show that these two contributions yield in some cases comparable or better results than standard sliding window detectors - despite its conceptual simplicity and run-time efficiency. Our third contribution is an application of prominent object detection, where we improve fine-grained categorization by pre-cropping images with the proposed approach. Finally, we also extend the proposed approach to detect multiple parts of rigid objects.

A model-based sequence similarity with application to handwritten word spotting.

This paper proposes a novel similarity measure between vector sequences. We work in the framework of model-based approaches, where each sequence is first mapped to a Hidden Markov Model (HMM) and then a measure of similarity is computed between the HMMs. We propose to model sequences with semicontinuous HMMs (SC-HMMs). This is a particular type of HMM whose emission probabilities in each state are mixtures of shared Gaussians. This crucial constraint provides two major benefits. First, the a priori information contained in the common set of Gaussians leads to a more accurate estimate of the HMM parameters. Second, the computation of a similarity between two SC-HMMs can be simplified to a Dynamic Time Warping (DTW) between their mixture weight vectors, which significantly reduces the computational cost. Experiments are carried out on a handwritten word retrieval task in three different datasets-an in-house dataset of real handwritten letters, the George Washington dataset, and the IFN/ENIT dataset of Arabic handwritten words. These experiments show that the proposed similarity outperforms the traditional DTW between the original sequences, and the model-based approach which uses ordinary continuous HMMs. We also show that this increase in accuracy can be traded against a significant reduction of the computational cost.