Abir Gallas

Effective Region-based Relevance Feedback for Interactive Content-based Image Retrieval

This paper proposes an effective framework for interactive region-based image retrieval. By utilizing fuzzy coarse segmentation and the graph structure for representing each image, the retrieval process was performed by measuring the image similarity according to the graph similarity. To assess the similarity between two graphs, fuzzy inter relations among regions feature vectors and spatial dispositions as well as fuzzy regions weights are explored. A region-based relevance feedback scheme was also incorporated into the retrieval process, by updating the importance of query image regions based on the user feedbacks, leading to a further performance improvement. Experimental study proves that the proposed region-based relevance feedback mechanism tailors the system semantic behavior relatively to each user personal preferences through the accumulation of the useful semantic information from the feedback information.

Image retrieval by comparison between complete oriented graphs of fuzzy regions

Images comparison is the most critical step in the content-based image retrieval process. Therefore, we propose in this paper our approach of comparison based on image modeling by complete oriented graph. This structure encompasses low-level region descriptors in nodes and coarse spatial disposition in edges. Each node is characterized by its wavelet transformation high frequency sub-band weighted by the region importance. Similarity degree between two images is identified thereafter by comparing their graphs using heuristics to guarantee low computational overhead and to resolve the NP-hard matching problem between graphs. The experimental results and comparison made with similar image retrieval engines indicate the robustness of the proposed approach for Wang dataset and prove the applied heuristics.

Locality-sensitive hashing for region-based large-scale image indexing

In this study, the authors present an efficient method for approximate large-scale image indexing and retrieval. The proposed method is mainly based on the visual content of the image regions. Indeed, regions are obtained by a fuzzy segmentation and they are described using high-frequency sub-band wavelets. Moreover, because of the difficulty in managing a huge amount of data, which is caused by the exponential growth of the processing time, approximate nearest neighbour algorithms are used to improve the retrieval speed. Therefore they adopted locality-sensitive hashing (LSH) for region-based indexing of images. In particular, since LSH performance depends fundamentally on the hash function partitioning the space, they exposed a new function, inspired from the E 8 lattice, that can efficiently be combined with the multi-probe LSH and the query-adaptive LSH . To justify the adopted theoretical choices and to highlight the efficiency of the proposed method, a set of experiments related to the region-based image retrieval are carried out on the challenging ‘Wang’ data set.

General bases indexing using approximate structure techniques

The emergence of numerical technologies requires the use of powerful tools and retrieval engines for fast and efficient access to images datasets. In spite of the rapid growth of computing performance, it is always difficult to manage huge amount of data because of the exponential growth of the processing time according to the data complexity. Therefore, in this paper, Approximate Nearest-Neighbor (ANN) algorithms are used as a solution of dramatically improving the retrieval speed. Indeed, we focus on locality-sensitive hashing (LSH) technique. Since its performance depends essentially on the hash function used to partition the space, we propose to introduce a new function inspired from the E8 lattice and to combine it with the Multi-Probe-LSH and the Query Adaptative LSH (QA-LSH). This method is applied in our case in the context of CBIR. In order to prove the robustness of the proposed approach, a set of experimental results are compared with similar state of the art algorithms.