Gibran Fuentes  Pineda

In most object recognition methods, the creation of object models requires human supervision such as manual object segmentation, image annotations or the number of di erent kinds of objects that appear in a given set of images. However, for large image sets, even a small amount of supervision can be extremely expensive. Moreover, the performance of these methods deteriorates with the number of images and/or the dimensionality of the image representation. These limitations make current methods unsuitable for exploiting the large amount of visual information available nowadays. This dissertation addresses the problem of e cient object discovery from images without supervision. Given a set of images, the goal is to automatically derive models that can be used to retrieve images that contain particular objects and to localize such objects within the images. We are interested in finding associations between images based on the objects they contain rather than on the whole image. We propose a new approach to discovering objects by searching for reoccurring patterns via hashing. In particular, we exploit locality sensitive hashing (LSH), a randomized algorithm for e cient similarity search. Because of the consistent use of hashing, such approach is highly e cient and suitable for large image sets. We develop two object discovery methods based on this approach. In our first method, region features are computed from the images by color segmentation. Then, objects are discovered by extracting frequent patterns of closely located regions. Here, we exploit hashing to (1) extract components by gathering near pixels of the same color, (2) label similar components based on their color and size, (3) generate object candidates by gathering closely located components and (4) find similar object candidates. This method can discover objects in simple scenes robustly against rotation, slide and small intra-class variations. Our second method represents each image as a set of vector quantized a ne covariant features. Object models are extracted by clustering features that consistently appear together in the same images under the assumption that they underlie the same object. The extraction of co-occurring features is e ciently implemented by Min-Hashing. The models derived by this method are highly discriminative and robust to clutter, occlusion and changes in scale, illumination and viewpoint. The proposed method could discover objects from a set of 101,922 images in just 38.35 minutes. In a quantitative evaluation using a benchmark dataset, this method had higher scores than state-of-the-art methods while being significantly faster.電気通信大学201

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Locality Sensitive Hashingに基く効率的なオブジェクト発見

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Locality Sensitive Hashingに基く効率的なオブジェクト発見

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Creative Repository of Electro-Communications

Creative Repository of Electro-Communications