BoVW model for animal recognition: an evaluation on SIFT feature strategies

Nowadays classifying images into categories have taken a lot of interests in both research and practice. Content Based Image Retrieval (CBIR) was not successful in solving semantic gap problem. Therefore, Bag of Visual Words (BoVW) model was created for quantizing different visual features into words. SIFT detector is invariant and robust to translation, rotations, scaling and partially invariant to affine distortion and illumination changes. The aim of this paper is to investigate the potential usage of BoVW Word model in animal recognition. The better SIFT feature extraction method for pictures of the animal was also specified. The performance evaluation on several SIFT feature strategies validates that MSDSIFT feature extraction will get better results

Exploiting Context Information for Image Description

Integrating ontological knowledge is a promising research direction to improve automatic image description. In particular, when probabilistic ontologies are available, the corresponding probabilities could be combined with the probabilities produced by a multi-class classifier applied to different parts in an image. This combination not only provides the relations existing between the different segments, but can also improve the classification accuracy. In fact, the context often gives cues suggesting the correct class of the segment. This paper discusses a possible implementation of this integration, and the first experimental results shows its effectiveness when the classifier accuracy is relatively low. For the assessment of the performance we constructed a simulated classifier which allows the a priori decision of its performance with a sufficient precision

Automatic Images Annotation Extension Using a Probabilistic Graphical Model

International audienceWith the fast development of digital cameras and social media image sharing, automatic image annotation has become a researcharea of great interest. It enables indexing, extracting and searching in large collections of images in an easier and faster way. In this paper, wepropose a model for the annotation extension of images using a probabilistic graphical model. This model is based on a mixture of multinomialdistributions and mixtures of Gaussians. The results of the proposed model are promising on three standard datasets: Corel-5k, ESP-Gameand IAPRTC-12

Image Annotation Using a Semantic Hierarchy

International audienc