retrieval model to text retrieval model, such that the well established textretrieval techniques can be utilized. Approaches of converting image feature vectors (IP domain) to weighted-term vectors (IR domain) are proposed in this paper. Furthermore, the relevance feedback technique fromthe

The state of the art in visual object retrieval from large databases is achieved by systems that are inspired by text retrieval. A key component of these approaches is that local regions of images are characterized using high-dimensional descriptors which are then mapped to “visual words ” selected

by what makes it different from other images. One drawback of the Fisher vector is that it is high-dimensional and, as opposed to the BOV, it is dense. The resulting memory and computational costs do not make Fisher vectors directly amenable to large-scale retrieval. Therefore, we compress Fisher vectors

histogram techniques such as high-dimensional feature vectors, spatial localization, indexing and distance computation. We present the binary color set back-projection technique and discuss its implementation in the VisualSEEk content-based image/video retrieval system for the World Wide Web. We also

dataset show that our approach sig-nificantly outperforms the state of the art, even for a very compact representation of 16 bytes per image. Retrieving category images is evaluated on the “web-queries ” dataset. We show that attribute features combined with Fisher vec-tors improve the performance

We introduce an extension of bag-of-words image representations to encode spatial layout. Using the Fisher kernel framework we derive a representation that encodes the spatial mean and the variance of image regions associated with visual words. We extend this representation by using a Gaussian

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This paper presents a new method for constructing models from a set of positive and negative sample images; the method requires no manual extraction of significant objects or features. Our model representation is based on two layers. The first one consists of “generic ” descriptors which represent

This paper proposes two novel image similarity measures for fast indexing via locality sensitive hashing. The similarity measures are applied and evaluated in the context of near duplicate image detection. The proposed method uses a visual vocabulary of vector quantized local feature descriptors

with initialization and poor convergence to boundary concavities. GVF is computed as a di#usion of the gradient vectors of a gray-level or binary edge map derived from the image. In this paper, we generalize the GVF formulation to include two spatially varying weighting functions. This improves active contour