Similarity-based Image Retrieval Research Articles

Similarity-based Image Retrieval considering Artifacts from Plural Key Images by Self-Organizing Map with Refractoriness

Similarity-based Image Retrieval considering Artifacts from Plural Key Images by Self-Organizing Map with Refractoriness

Indexing for multipoint interactive similarity retrieval in iconic spatial image databases

Similarity-based retrieval of images is an important task in many image database applications. Interactive similarity retrieval is one way to resolve the fuzzy area involving psychological and physiological factors of individuals during the retrieval process. A good interactive similarity system depends not only on a good similarity measure, but also on the structure of the image database and the related retrieval process. In this paper, we propose to use a dynamic similarity measure on top of the enhanced digraph index structure for interactive iconic image similarity retrieval. Our approach makes use of the multiple feedbacks from the user to get the hidden subjective information of the retrieval, and avoids the high cost of re-computation of an interactive retrieval algorithm.

COLOR IMAGE SEMANTIC INFORMATION RETRIEVAL SYSTEM USING HUMAN SENSATION AND EMOTION

Most of the content-based image retrieval systems focus on similarity-based retrieval of images by utilizing color, shape and texture features. In this paper we propose a new searching scheme, called FMV Indexing, to guarantee higher retrieval quality. This scheme allows us to retrieve images based on high-level semantic concepts such as “cool,” “soft,” “strong,” etc. Our experimental results show that proposed techniques can facilitate users’ searching intentions more accurately and give a more favorable feeling of satisfaction to users.

Similarity-based image retrieval system using partitioned iterated function system codes

We propose a new image retrieval system using partitioned iterated function system (PIFS) codes. In PIFS encoding, a compression code contains mapping information between similar regions in the same image. This mapping information can be treated as vectors, and representative vectors can be generated using them. Representative vectors describe the features of the image. Hence, the similarity between images is directly calculable from representative vectors. This similarity is applicable to image retrieval. In this article, we explain this scheme and demonstrate its efficiency experimentally.

Similarity-based image retrieval system using partitioned iterated function system codes

Similarity-based image retrieval system using partitioned iterated function system codes

Image database indexing using JPEG coefficients

Image database indexing is used for efficient retrieval of images in response to a query expressed as an example image. The query image is processed to extract information that is matched against the index to provide pointers to similar images. We present a technique that facilitates content similarity-based retrieval of jpeg-compressed images without first having to uncompress them. The technique is based on an index developed from a subset of jpeg coefficients and a similarity measure to determine the difference between the query image and the images in the database. This method offers substantial efficiency as images are processed in compressed format, information that was derived during the original compression of the images is reused, and extensive early pruning is possible. Initial experiments with the index have provided encouraging results. The system outputs a set of ranked images in the database with respect to the query using the similarity measure, and can be limited to output a specified number of matched images by changing the threshold match.

Content-based Image Retrieval by Spatial Similarity

Similarity-based retrieval of images is an important task in image databases. Most of the user's queries are on retrieving those database images that are spatially similar to a query image. In defence strategies, one wants to know a number of armoured vehicles, such as battle tanks, portable missile launching vehicles, etc. moving towards it, so that one can decide counter strategy. Content-based spatial similarity retrieval of images can be used to locate spatial relationship of various objects in a specific area from the aerial photographs and to retrieve images similar to the query image from image database. A content-based image retrieval system that efficiently and effectively retrieves information from a defence image database along with the architecture for retrieving images by spatial similarity is presented. A robust algorithm SIMdef for retrieval by spatial similarity is proposed that utilises both directional and topological relations for computing similarity between images, retrieves similar images and recognises images even after they undergo modelling transformations (translation, scale and rotation). A case study for some of the common objects, used in defence applications using SIMdef algorithm, has been done.

Image retrieval based on object's orientation spatial relationship

Similarity-based retrieval of images is an important task in many image database applications. A major class of users' requests requires retrieving those images in the database that are spatially similar to the query image. We propose an approach for computing the orientation spatial similarity between two symbolic images in this paper. The proposed approach is not only rotation invariant, but also captures the relative distance and orientation range between objects.

情報の可視化 画像群の意味的な可視化表現を用いた画像検索システム

We have developed a similarity-based image retrieval system that represents retrieved images as a scatter diagram a semantic space. One axis of the space shows the suitability of a keyword assigned to each image. The suitabilities are estimated by linearly transforming the image features of the retrieved data, and the coefficients of the transformation are determined by multiple regression analysis. First, a-user selects images as key images. Then the system retrieves images and calculates the estimated suitablities variance among the retrieved images for each keyword, and presents the user keywords that give the large variances. Next, the user selects a keyword from the given keywords for each axis. Finally, the system displays the retrieved images in the semantic space spanned by the two axes. The system enables the user to apply the center of gravity among the features of the key images to the retrieval. The system thus effectively assists the user in retrieving images by integrating the semantic visualization and the center of gravity retrieval. Testing of the keyword suitability estimation showed that the estimation precision became high by using keywords with the large suitabilites variances.

Design and evaluation of algorithms for image retrieval by spatial similarity

Similarity-based retrieval of images is an important task in many image database applications. A major class of users' requests requires retrieving those images in the database that are spatially similar to the query image. We propose an algorithm for computing the spatial similarity between two symbolic images. A symbolic image is a logical representation of the original image where the image objects are uniquely labeled with symbolic names. Spatial relationships in a symbolic image are represented as edges in a weighted graph referred to as spatial-orientation graph. Spatial similarity is then quantified in terms of the number of, as well as the extent to which, the edges of the spatial-orientation graph of the database image conform to the corresponding edges of the spatial-orientation graph of the query image. The proposed algorithm is robust in the sense that it can deal with translation, scale, and rotational variances in images. The algorithm has quadratic time complexity in terms of the total number of objects in both the database and query images. We also introduce the idea of quantifying a system's retrieval quality by having an expert specify the expected rank ordering with respect to each query for a set of test queries. This enables us to assess the quality of algorithms comprehensively for retrieval in image databases. The characteristics of the proposed algorithm are compared with those of the previously available algorithms using a testbed of images. The comparison demonstrated that our algorithm is not only more efficient but also provides a rank ordering of images that consistently matches with the expert's expected rank ordering.