Andres Dorado

Semi-automatic image annotation using frequent keyword mining

Research in content-based image retrieval is an expanding discipline with an accelerated growth in the last ten years. Advances in telecommunications and the huge demand of visual information on Internet and mobile devices is occupying the attention of the researchers in developing efficient systems to ease the task of useful visual information retrieval by the users. We present a semiautomatic image annotation process using the low-level image descriptor fuzzy color signature to extract the most similar images from an annotated database and frequent pattern mining to select the candidates keywords for annotating the new image. The idea is aimed at establishing a bridge between visual data and their interpretation using a weak semantic approach.

Semantic labeling of images combining color, texture and keywords

Content-based image retrieval systems combine perceptual features such as color, texture and shape with semantic concepts for improving the quality of the querys results. In this paper, an annotation technique that combines color and texture with keywords is presented. A method based on color similarity along with a keyword mining technique is used to propagate keywords extracted from a sub-set of annotated images into a large-scale database. A method based on texture properties is applied to link keywords with regions within the images. Finally, an approach for semantic labeling of images is described. In this approach, accuracy of the annotations is estimated and the relationships among keywords are identified. The presented annotation technique is useful for labeling images with keywords construing the underlying semantic content.

Fuzzy color signatures

With the large and increasing amount of visual information available in digital libraries and the Web, efficient and robust systems for image retrieval are urgently needed. A compact color descriptor scheme and an efficient metric to compare and retrieve images is presented. An image adaptive color clustering method, called fuzzy color signature, is proposed. The original image colors are mapped into a small number of representative colors using a peaks detection function derived from the color distribution. Fuzzy color signatures are then used as image descriptors. To compare image descriptors the earth movers distance is used. Several experiments have been conducted to assess the performance of the proposed technique.

Dimensionality reduction for content-based image classification

Effective ways of organizing image descriptors is a critical design step of content-based image classification systems. Suitable descriptors are selected according to the problem domain for generating the feature space. Using several descriptors improves accuracy of representation but risen some challenges such as non linear combination, expensive computation and the curse of dimensionality. In This work an approach using a non parametric statistical test for effective dimensionality reduction is presented. The proposed method facilitates feature discrimination and keeps relevant information.

Climbing the semantic ladder: towards semantic semi-automatic image annotation using MPEG-7 descriptor schemas

The fast development of innovative tools to create user friendly and effective multimedia libraries, services and environments requires novel concepts to support storage, annotation and retrieval of huge amounts of digital audiovisual data. This article presents a technique to tackle the first instance of the problem in visual digital archives: classification using generic semantic descriptions. As a case study, classification abilities inherent to some important MFEG-7 low-level visual descriptors are explored and quantified.

User-driven fuzzy clustering: on the road to semantic classification

The work leading to this paper is semantic image classification. The aim is to evaluate contributions of clustering mechanisms to organize low-level features into semantically meaningful groups whose interpretation may relate to some description task pertaining to the image content. Cluster assignment reveals underlying structures in the data sets without requiring prior information. The semantic component indicates that some domain knowledge about the classification problem is available and can be used as part of the training procedures. Besides, data structural analysis can be applied to determine proximity and overlapping between classes, which leads to misclassification problems. This information is used to guide the algorithms towards a desired partition of the feature space and establish links between visual primitives and classes. It derives into partially supervised learning modes. Experimental studies are addressed to evaluate how unsupervised and partially supervised fuzzy clustering boost semantic-based classification capabilities.

Exploiting problem domain knowledge for accurate building image classification

An approach for classification of building images through rule-based fuzzy inference is presented. It exploits rough matching and problem domain knowledge to improve precision results. This approach uses knowledge representation based on a fuzzy reasoning model for establishing a bridge between visual primitives and their interpretations.