In this paper, two extended versions of motif co-occurrence matrices (MCM) are derived and concatenated for efficient content-based image retrieval (CBIR). This paper divides the image into 2 x 2 grids. Each 2 x 2 grid is replaced with two different Peano scan motif (PSM) indexes, one is initiated from top left most pixel and the other is initiated from bottom right most pixel. This transforms the entire image into two different images and co-occurrence matrices are derived on these two transformed images: the first one is named as "motif co-occurrence matrix initiated from top left most pixel (MCM TL )" and second one is named as "motif cooccurrence matrix initiated from bottom right most pixel (MCM BR )". The proposed method concatenates the feature vectors of MCM TL and MCM BR and derives multi motif co-occurrence matrix (MMCM) features. This paper carried out investigation on image databases i.e. Corel-1k, Corel-10k, MIT-VisTex, Brodtaz, and CMU-PIE and the results are compared with other well-known CBIR methods. The results indicate the efficacy of the proposed MMCM than the other methods and especially on MCM [19] method.

We present a new technique for content based image retrieval by deriving a Local motif pattern (LMP) code co-occurrence matrix (LMP-CM). This paper divides the image into 2 x 2 grids. On each 2 x 2 grid two different Peano scan motif (PSM) indexes are derived, one is initiated from top left most pixel and the other is initiated from bottom right most pixel. From these two different PSM indexes, this paper derived a unique LMP code for each 2 x 2 grid, ranges from 0 to 35. Each PSM minimizes the local gradient while traversing the 2 x 2 grid. A co-occurrence matrix is derived on LMP code and Grey level co-occurrence features are derived for efficient image retrieval. This paper is an extension of our previous MMCM approach [54]. Experimental results on popular databases reveal an improvement in retrieval rate than existing methods.

Invention of the digital camera and also cell phones with powerful cameras with moderate and low pricing system has given the common man the privilege to capture his world in pictures anywhere, at any time, and conveniently share them with others. This has resulted the generation of volumes of images. These factors have created numerous possibilities and finally created interest among the researchers towards the design of an efficient and accurate Content Based Information Retrieval (CBIR) system. That's why new technological advances and growth in CBIR has been unquestionably rapid during the last five years. Various face recognition methods are derived using local features, and among them the Local Binary Pattern (LBP) approach is very famous. The basic disadvantage of these methods is they completely fail in representing features derived from large or macro structures or regions, which are very much essential for faces. To address this present paper proposes a median based multi region LBP. The proposed median based multi region LBP, initially divides the facial image in to nonoverlapped regions of size 5 x 5. LBP values are evaluated by dividing the region in to sub regions of size 3 x 3. The 9 subregion LBP values are arranged in the sorted manner and the median LBP code is considered as the feature vector for the region. The present paper also proposes the minimum and maximum based regional LBP methods for efficient image retrieval. To overcome the noise and illumination effect the proposed method initially applied DOG preprocessing method with gamma correction. The proposed method is applied on FG-NET and Goggle databases for efficient facial image retrieval. The experimental results indicate the efficiency of the proposed method.

Functional and anatomical information extraction from Magnetic Resonance Images (MRI) is important in medical image applications. The information extraction is highly influenced by the artifacts in the MRI images. The feature extraction involves the segmentation of MRI images. We present a MRI image segmentation using Bat Optimization Algorithm (BOA) with Fuzzy C Means (FCM) clustering. Echolocation of bats is utilized in Bat Optimization Algorithm. The proposed segmentation technique is evaluated with existing segmentation techniques. Results of experimentation shows that proposed segmentation technique outperforms existing methods and produces 98.5% better results.

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