AN APPROACH FOR SUPERVISED SEMANTIC ANNOTATION

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1 AN APPROACH FOR SUPERVISED SEMANTIC ANNOTATION A. DORADO AND E. IZQUIERDO Queen Mary, University of London Electronic Engineering Department Mile End Road, London E1 4NS, U.K. {andres.dorado, Advanced Content-Based Image Retrieval Systems combine automatic extracted low-level features with user-specified interpretations to generate semantic descriptors, which allow to conceptualize queries in a search engine. The user interpretations can be summarized by means of symbols such as keywords. In this paper, a supervised image annotation process is presented. This process combines color and texture features with symbolic descriptions for semi-automatic and incremental annotation of images. In this way, images can be retrieved using a semantic concept human beings are familiar with. 1. Introduction Advanced CBR systems combine automatic extracted low-level features with high-level concepts to overcome the semantic gap. These systems incorporate reasoning and learning capabilities to establish an intelligent architecture, which enable them to produce effective and accurate query results according to the requests of the end-users. An approach is to generate metadata-based descriptors associating image properties to user interpretations. However, in order to achieve efficient results it requires contributions from fields such as cognitive psychology, artificial intelligence, and semiotics 1. User interpretations, known as semantic concepts, can be summarized by means of symbols such as keywords and icons. The association of these symbols (keywords or tags) to the images for labelling their semantic content is known as image annotation. In this paper, a supervised image annotation process is presented. This process extracts color and texture properties for inter- and intra-image analysis, respectively. Salient features are combined with symbolic descriptions for semi-automatic and incremental annotation of images. The result is a set of labels describing the image content. 1

2 2 2. Annotations: Metalevel notations or Metadescriptors The term annotation refers to the use of auxiliary symbols that are used to modify the interpretation of other symbols. These annotation symbols typically do not have the same kind of meaning as the symbols that they annotate 2. On the other hand, semantic annotation is augmentation of data to facilitate automatic recognition of the underlying semantic structure. This kind of annotation is expressed by means of symbol s structures, where each symbol is either a keyword or an icon. Ambiguous interpretations are avoided using a symbol domain, which is organized into a lexicon. 3. Supervised Image Annotation Process The supervised image annotation process can be divided into two main stages: the labels selection and the forward propagation. The first establishes the basic relations among images and an user-specified lexicon facilitating keyword-based image retrieval. The second allows to generate new relations for other images useful for keyword-based image indexing Labels Selection Stage This stage generates a set of candidate keywords for the annotation of an image. It can be summarized as follows: Let X = {x 0,..., x n 1 } be a set of n images and K = {k 0,..., k m 1 } be an user-specified lexicon consisting of m keywords. The annotation process A defined as X K A Z (1) generates a set of labels Z = {z ij z ij : x i k j } where x i X, k j K and z ij is a label annotating image x i with the keyword k j. User feedback is needed to determine the accuracy of the labels. So, the user assign a weight, w [0, 100], to the label z ij interpreting the relation between x i and k j. Although this manual process is time consuming and looks less-effective, interaction is necessary for keyword disambiguation. In addition, semantic interpretations require an interplay among the user and the images Forward Propagation Stage The second stage instantiates the labels for new images. Given an image q, the set of keywords for annotating q are selected by means of two steps:

3 3 an inter-image color-based similarity analysis and an intra-image texturebased analysis. Using a similarity-based retrieval method, the k-nearest-neighbors (KNN) images in X with respect to q are organized into a ranking vector knn(q). These images satisfy S(f q, f x ) ɛ 0 where S( ) is a metric to measure the similarity between two feature vectors, f q, f x, and ɛ 0 is an user-specified constraint. The support of a set of labels Z, s(z), is defined as the ratio of the number of images that are referred by Z to the total of images. A set of labels Z is said to be frequent if s(z) is higher than a specified support threshold. Applying a data mining technique on the set of labels associated with the KNN images to q, the frequent labels can be identified. These labels contain candidate keywords to annotate q. In order to improve the annotation process, an intra-image analysis is done. So, the image q is partitioned into n equal-size rectangular regions R(q) = {r 0 (q),..., r n 1 (q)} Such that q = n 1 j=0 r j (q) (2) A homogeneous texture vector t j = f r j (q) is generated for each region. The amount of regions is reduced using a clustering technique, which groups regions based on the similarity of the texture properties. For the next step, the following is assumed: Let C be a codebook consisting of a set of codewords c. Each codeword c contains a tuple s, k, where s is a synthetic feature vector with representative values for a homogeneous texture and k K is a keyword of the user-specified lexicon labelling s. For each vector t j the corresponding keyword k is extracted from the codebook by means of a classification technique. The result is a set of candidate keywords to annotate q. The accuracy of the keyword corresponds to the distance S(t j, s). The probability P (k q) that the keyword k will be the correct label for the image q can be expressed as P (k q) = min k j K c(q) f( j k j ) (3) where K c (q) is the set of candidate keywords, k j calculates the corresponding accuracy. is a keyword and f( )

4 4 The keyword k is accepted if P (k q) ɛ 1, where ɛ 1 is an user-specified constraint. In this way, a meta-relation between q and the accepted keywords denoted by q z q is established. Finally, an user-supervised step is recommended for tuning the set of labels and adjust the accuracy values. The Annotation Error Rate 4 can be used to measure the process performance: Annotation Error Rate = Number of incorrectly assigned keywords Total number of keywords (4) On the other hand, the confidence of the annotation can be estimated by using the Keyword Error Rate 4 Keyword Error Rate = Substitutions + Insertions Total number of candidates keywords (5) 4. Experimental Results Several multi-spectral images from the CorelDraw database were used in experimental. The process was applied to three-band images, size of 192x128 pixels in JPEG format. A sample image is shown in the Figure 1 with its corresponding partition and merged regions. Figure 1. Sample Image Using a similarity-based retrieval method, the system got the KNN images presented in the Figure 2. The frequent keywords {mountains, sky, daytime} were found in the KNN images. On the other hand, the keywords {sky, mountains, vegetation} were found after the intra-image analysis. Therefore, the main content of the image consists of a scene including sky and mountains.

5 5 # #15036 # #0002 #17031 #18069 # # Figure 2. K-Nearest Neighbor Images 5. Conclusions and Further Work To improve the precision of the forward propagation stage, it should be applied on images of the same class (i.e. grouped by domain). This image annotation technique facilitates sub-images retrieval using text-based queries, which are very useful and closer to the way humans conceptualize the content of pictures. User relevance feedback improves the accuracy of the process for further annotations. In addition, this interaction allows the symbol domain can be enhanced with new keywords. Intelligent Content-based Image Retrieval systems use simple structures such as list of keywords as a first step towards complex descriptions establishing a foundation for ontology-based semantic annotation. References 1. A.B. Benitez and J.R. Smith. New Frontiers for Intelligent Content-Based Retrieval. In Storage and Retrieval for Image and Video Databases, SPIE, San Jose, CA. Jan M. Stefik. Introduction to Knowledge Systems. Morgan Kaufmann Publishers Inc Arnold W.M. Smeulders, Marcel Worring, Simone Santini, Amarnath Gupta, and Ramesh Jain. Content-Based Image Retrieval at the End of the Early Years. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 12, pp December R. Zhang and A.I. Rudnicky. Word Level Confidence Annotation using Combinations of Features. EuroSpeech Scandinavia.

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