Recognizing white blood cells with local image descriptors

Automatic and reliable classification of images of white blood cells is desirable for inexpensive, quick and accurate health diagnosis worldwide. In contrast to previous approaches which tend to rely on image segmentation and a careful choice of ad hoc (geometric) features, we explore the possibilities of local image descriptors, since they are a simple approachthey require no explicit segmentation, and yet they have been shown to be quite robust against background distraction in a number of visual tasks. Despite its potential, this methodology remains unexplored for this problem. In this work, images are therefore characterized with the well-known visual bag-of-words approach. Three keypoint detectors and five regular sampling strategies are studied and compared. The results indicate that the approach is encouraging, and that both the sparse keypoint detectors and the dense regular sampling strategies can perform reasonably well (mean accuracies of about 80% are obtained), and are competitive to segmentation-based approaches. Two of the main findings are as follows. First, for sparse points, the detector which localizes keypoints on the cell contour (oFAST) performs somehow better than the other two (SIFT and CenSurE). Second, interestingly, and partly contrary to our expectations, the regular sampling strategies including hierarchical spatial information, multi-resolution encoding, or foveal-like sampling, clearly outperform the two simpler uniform-sampling strategies considered. From the broader perspective of expert and intelligent systems, the relevance of the proposed approach is that, since it is very general and problem-agnostic, it makes unnecesary human expertise to be elicited in the form of explicit visual cues; only the labels of the cell type are required from human domain experts

Multiple Instance Learning: A Survey of Problem Characteristics and Applications

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research

Image Reconstruction from Bag-of-Visual-Words

The objective of this work is to reconstruct an original image from Bag-of-Visual-Words (BoVW). Image reconstruction from features can be a means of identifying the characteristics of features. Additionally, it enables us to generate novel images via features. Although BoVW is the de facto standard feature for image recognition and retrieval, successful image reconstruction from BoVW has not been reported yet. What complicates this task is that BoVW lacks the spatial information for including visual words. As described in this paper, to estimate an original arrangement, we propose an evaluation function that incorporates the naturalness of local adjacency and the global position, with a method to obtain related parameters using an external image database. To evaluate the performance of our method, we reconstruct images of objects of 101 kinds. Additionally, we apply our method to analyze object classifiers and to generate novel images via BoVW