K-NEAREST NEIGHBORS DIRECTED SYNTHETIC IMAGES INJECTION
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Wiamis2010 poster

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It is widely acknowledged that good performances of content-based image retrieval systems can be attained by adopting relevance feedback mechanisms. One of the main difficulties in exploiting relevance information is the availability of few relevant images, as users typically label a few dozen of images, the majority of them often being non-relevant to user’s needs. In order to boost the learning capabilities of relevance feedback techniques, this paper proposes the creation of points in the feature space which can be considered as representation of relevant images. The new points are generated taking into account not only the available relevant points in the feature space, but also the relative positions of non-relevant ones. This approach has been tested on a relevance feedback technique, based on the Nearest-Neighbor classification paradigm. Reported experiments show the effectiveness of the proposed technique relatively to precision and recall.

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Wiamis2010 poster

  1. 1. K-NEAREST NEIGHBORS DIRECTED SYNTHETIC IMAGES INJECTION Luca Piras, Giorgio Giacinto Dept. of Electrical and Electronic Engineering - University of Cagliari, Italy luca.piras@diee.unica.it - giacinto@diee.unica.it K-Nearest Neighbors Directed Pattern Injection Relevance feedback We propose to address the problem of small number of training Relevance feedback techniques are employed to capture the patterns by artificially increasing the number of relevant patterns in subjectivity of retrieval results and to refine the search. The user is asked the feature space. The basic idea underlying K-Nearest Neighbors to label the images as being relevant or not, and the similarity measure Directed Pattern Injection consists in adding some artificial patterns is modified accordingly: the images in the database are ranked in generated in the feature space by taking into account the K terms of the distances from the nearest relevant and non-relevant relevant images nearest to a reference point in the feature space. images. I ! NN nr I () 1 K $ # X % Qk ( ) relevanceNN I = () Pi = X + ! " () I ! NN r I + I ! NN nr I () K k =1 k The choice of all parameter is far from being a trivial task; the NN r ( I ) ( resp., NN ( I ))nr nearest relevant (resp., non-relevant) image to image I. effectiveness of the method depends from: X Reference relevant image K n° Nearest Neighbors ! k ! N (0,1) - Query Digital Compute the new Library query using the - mean vector of all the relevant images (Mr) user's hints Non-relevant Relevant - each relevant images (Ar) - a point computed according to the BQS New artificial query movement technique (BQS) pattern Compute the similarity between the query image and the images in the database Example-Image ! Scaling factor chosen by a user to 1 perform a search in a != Digital Library The user marks the relevant and non-relevant ("1 2 + " 22 ) Bound of linear images for the relevance feedback 1 != combination ("1 + "2 ) After several iterations 1 Bound of nearest != relevant image ("1 + "2 ) 2 Bound of farthest relevant image The system outputs the results Qk k-th relevant image nearest to X N where n = 2, …, 5 Number of pattern Pi : =n R+ P Relevant images found at the previous steps Lack of relevant images One of the most severe problems in exploiting relevance feedback in image retrieval is the small number of images that the user considers as being “relevant” compared to the number of non-relevant images especially during the first iterations. Experimental results The Caltech-256 dataset obtained from the California Institute of I) Even in the case of very “cooperative” users, it is not feasible to Technology repository has been used. It consists of 30607 images display more than a few dozens of images to label. subdivided into 257 semantic classes and the number of images per class ranges from 80 to 827. II) If the database at hand is very large, then the number of images that are relevant to the query can easily be very small compared to 500 images have been randomly extracted from all of the 257 classes, the size of the database. and used as query. The top twenty nearest neighbors of each query III) In a high dimensional feature space images with different are returned. 9 relevance feedback iterations are performed. semantic content can lie near each other. The Tamura representation has been used (18-dimensional feature vector). 1 Our proposal F= 1 1 Increasing the number of “relevant” patterns used to train the system: + 2 ! prec 2 ! recall I) Creating new random artificial patterns by exploiting nearest neighbor information. II) Constraining these patterns in a region of the feature space containing relevant images. Pattern Recognition and Applications Group P R A Group http://prag.diee.unica.it/pra/eng/home WIAMIS 2010

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